diff --git a/opendc-experiments/opendc-experiments-base/build.gradle.kts b/opendc-experiments/opendc-experiments-base/build.gradle.kts
index 30510785a..303517aa9 100644
--- a/opendc-experiments/opendc-experiments-base/build.gradle.kts
+++ b/opendc-experiments/opendc-experiments-base/build.gradle.kts
@@ -27,6 +27,7 @@ plugins {
     `kotlin-library-conventions`
     `testing-conventions`
     `jacoco-conventions`
+    distribution
     kotlin("plugin.serialization") version "1.9.22"
 }
 
@@ -35,6 +36,8 @@ dependencies {
     api(projects.opendcCompute.opendcComputeService)
     api(projects.opendcCompute.opendcComputeSimulator)
 
+    implementation(libs.clikt)
+
     implementation("org.jetbrains.kotlinx:kotlinx-serialization-json:1.6.0")
     implementation(libs.progressbar)
     implementation(project(mapOf("path" to ":opendc-simulator:opendc-simulator-core")))
@@ -48,3 +51,36 @@ dependencies {
     runtimeOnly(libs.log4j.core)
     runtimeOnly(libs.log4j.slf4j)
 }
+
+val createScenarioApp by tasks.creating(CreateStartScripts::class) {
+    dependsOn(tasks.jar)
+
+    applicationName = "OpenDCScenarioRunner"
+    mainClass.set("org.opendc.experiments.base.ScenarioCLI")
+    classpath = tasks.jar.get().outputs.files + configurations["runtimeClasspath"]
+    outputDir = project.buildDir.resolve("scripts")
+}
+
+// Create custom Scenario distribution
+distributions {
+    main {
+        distributionBaseName.set("OpenDCScenarioRunner")
+
+        contents {
+            from("README.md")
+            from("LICENSE.txt")
+            from("../../LICENSE.txt") {
+                rename { "LICENSE-OpenDC.txt" }
+            }
+
+            into("bin") {
+                from(createScenarioApp)
+            }
+
+            into("lib") {
+                from(tasks.jar)
+                from(configurations["runtimeClasspath"])
+            }
+        }
+    }
+}
diff --git a/opendc-experiments/opendc-experiments-scenario/src/main/kotlin/org/opendc/experiments/scenario/ScenarioCli.kt b/opendc-experiments/opendc-experiments-base/src/main/kotlin/org/opendc/experiments/base/runner/ScenarioCli.kt
similarity index 96%
rename from opendc-experiments/opendc-experiments-scenario/src/main/kotlin/org/opendc/experiments/scenario/ScenarioCli.kt
rename to opendc-experiments/opendc-experiments-base/src/main/kotlin/org/opendc/experiments/base/runner/ScenarioCli.kt
index d71f41415..abf9ad467 100644
--- a/opendc-experiments/opendc-experiments-scenario/src/main/kotlin/org/opendc/experiments/scenario/ScenarioCli.kt
+++ b/opendc-experiments/opendc-experiments-base/src/main/kotlin/org/opendc/experiments/base/runner/ScenarioCli.kt
@@ -22,7 +22,7 @@
 
 @file:JvmName("ScenarioCli")
 
-package org.opendc.experiments.scenario
+package org.opendc.experiments.base.runner
 
 import com.github.ajalt.clikt.core.CliktCommand
 import com.github.ajalt.clikt.parameters.options.default
@@ -30,7 +30,6 @@ import com.github.ajalt.clikt.parameters.options.defaultLazy
 import com.github.ajalt.clikt.parameters.options.option
 import com.github.ajalt.clikt.parameters.types.file
 import com.github.ajalt.clikt.parameters.types.int
-import org.opendc.experiments.base.runner.runScenarios
 import org.opendc.experiments.base.scenario.getScenarios
 import java.io.File
 
diff --git a/opendc-experiments/opendc-experiments-base/src/main/kotlin/org/opendc/experiments/base/runner/ScenarioHelpers.kt b/opendc-experiments/opendc-experiments-base/src/main/kotlin/org/opendc/experiments/base/runner/ScenarioReplayer.kt
similarity index 100%
rename from opendc-experiments/opendc-experiments-base/src/main/kotlin/org/opendc/experiments/base/runner/ScenarioHelpers.kt
rename to opendc-experiments/opendc-experiments-base/src/main/kotlin/org/opendc/experiments/base/runner/ScenarioReplayer.kt
diff --git a/opendc-experiments/opendc-experiments-scenario/build.gradle.kts b/opendc-experiments/opendc-experiments-scenario/build.gradle.kts
deleted file mode 100644
index d44a0aa79..000000000
--- a/opendc-experiments/opendc-experiments-scenario/build.gradle.kts
+++ /dev/null
@@ -1,96 +0,0 @@
-/*
- * Copyright (c) 2019 AtLarge Research
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to deal
- * in the Software without restriction, including without limitation the rights
- * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
- * copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-
-description = "Experiments for the Scenario work"
-
-// Build configuration
-plugins {
-    `kotlin-library-conventions`
-    `kotlin-conventions`
-    `testing-conventions`
-    `jacoco-conventions`
-    `benchmark-conventions`
-    distribution
-    kotlin("plugin.serialization") version "1.9.22"
-}
-
-dependencies {
-    implementation(projects.opendcSimulator.opendcSimulatorCore)
-    implementation(projects.opendcSimulator.opendcSimulatorCompute)
-    implementation(projects.opendcCompute.opendcComputeSimulator)
-
-    implementation(libs.clikt)
-    implementation(libs.progressbar)
-    implementation(libs.kotlin.logging)
-
-    implementation(libs.jackson.module.kotlin)
-    implementation("org.jetbrains.kotlinx:kotlinx-serialization-json:1.6.0")
-
-    implementation(project(mapOf("path" to ":opendc-compute:opendc-compute-telemetry")))
-    implementation(project(mapOf("path" to ":opendc-compute:opendc-compute-topology")))
-    implementation(project(mapOf("path" to ":opendc-compute:opendc-compute-workload")))
-    implementation(project(mapOf("path" to ":opendc-experiments:opendc-experiments-base")))
-
-    runtimeOnly(libs.log4j.core)
-    runtimeOnly(libs.log4j.slf4j)
-}
-
-val createScenarioApp by tasks.creating(CreateStartScripts::class) {
-    dependsOn(tasks.jar)
-
-    applicationName = "scenario"
-    mainClass.set("org.opendc.experiments.scenario.ScnearioCLI")
-    classpath = tasks.jar.get().outputs.files + configurations["runtimeClasspath"]
-    outputDir = project.buildDir.resolve("scripts")
-}
-
-// Create custom Greenifier distribution
-distributions {
-    main {
-        distributionBaseName.set("scenario")
-
-        contents {
-            from("README.md")
-            from("LICENSE.txt")
-            from("../../LICENSE.txt") {
-                rename { "LICENSE-OpenDC.txt" }
-            }
-
-            into("bin") {
-                from(createScenarioApp)
-            }
-
-            into("lib") {
-                from(tasks.jar)
-                from(configurations["runtimeClasspath"])
-            }
-
-            into("resources") {
-                from("src/main/resources")
-            }
-
-            into("Python_scripts") {
-                from("src/main/Python_scripts")
-            }
-        }
-    }
-}
diff --git a/opendc-experiments/opendc-experiments-scenario/src/jmh/kotlin/org/opendc/experiments/greenifier/GreenifierBenchmarks.kt b/opendc-experiments/opendc-experiments-scenario/src/jmh/kotlin/org/opendc/experiments/greenifier/GreenifierBenchmarks.kt
deleted file mode 100644
index 6cc6df365..000000000
--- a/opendc-experiments/opendc-experiments-scenario/src/jmh/kotlin/org/opendc/experiments/greenifier/GreenifierBenchmarks.kt
+++ /dev/null
@@ -1,95 +0,0 @@
-/*
- * Copyright (c) 2021 AtLarge Research
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to deal
- * in the Software without restriction, including without limitation the rights
- * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
- * copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-
-package org.opendc.experiments.greenifier
-
-import org.opendc.compute.service.ComputeService
-import org.opendc.compute.service.scheduler.FilterScheduler
-import org.opendc.compute.service.scheduler.filters.ComputeFilter
-import org.opendc.compute.service.scheduler.filters.RamFilter
-import org.opendc.compute.service.scheduler.filters.VCpuFilter
-import org.opendc.compute.service.scheduler.weights.CoreRamWeigher
-import org.opendc.compute.simulator.provisioner.Provisioner
-import org.opendc.compute.simulator.provisioner.setupComputeService
-import org.opendc.compute.simulator.provisioner.setupHosts
-import org.opendc.compute.topology.HostSpec
-import org.opendc.compute.topology.clusterTopology
-import org.opendc.compute.workload.ComputeWorkloadLoader
-import org.opendc.compute.workload.VirtualMachine
-import org.opendc.compute.workload.trace
-import org.opendc.experiments.base.runner.replay
-import org.opendc.simulator.kotlin.runSimulation
-import org.openjdk.jmh.annotations.Benchmark
-import org.openjdk.jmh.annotations.Fork
-import org.openjdk.jmh.annotations.Measurement
-import org.openjdk.jmh.annotations.Param
-import org.openjdk.jmh.annotations.Scope
-import org.openjdk.jmh.annotations.Setup
-import org.openjdk.jmh.annotations.State
-import org.openjdk.jmh.annotations.Warmup
-import java.io.File
-import java.util.Random
-import java.util.concurrent.TimeUnit
-
-/**
- * Benchmark suite for the Greenifier experiments.
- */
-@State(Scope.Thread)
-@Fork(1)
-@Warmup(iterations = 2, time = 5, timeUnit = TimeUnit.SECONDS)
-@Measurement(iterations = 5, time = 5, timeUnit = TimeUnit.SECONDS)
-class GreenifierBenchmarks {
-    private lateinit var vms: List<VirtualMachine>
-    private lateinit var topology: List<HostSpec>
-
-    @Param("true", "false")
-    private var isOptimized: Boolean = false
-
-    @Setup
-    fun setUp() {
-        val loader = ComputeWorkloadLoader(File("src/test/resources/trace"))
-        vms = trace("bitbrains-small").resolve(loader, Random(1L))
-        topology = checkNotNull(object {}.javaClass.getResourceAsStream("/topology.txt")).use { clusterTopology(it) }
-    }
-
-    @Benchmark
-    fun benchmarkGreenifier() =
-        runSimulation {
-            val serviceDomain = "compute.opendc.org"
-
-            Provisioner(dispatcher, seed = 0).use { provisioner ->
-                val computeScheduler =
-                    FilterScheduler(
-                        filters = listOf(ComputeFilter(), VCpuFilter(16.0), RamFilter(1.0)),
-                        weighers = listOf(CoreRamWeigher(multiplier = 1.0)),
-                    )
-
-                provisioner.runSteps(
-                    setupComputeService(serviceDomain, { computeScheduler }),
-                    setupHosts(serviceDomain, topology, optimize = isOptimized),
-                )
-
-                val service = provisioner.registry.resolve(serviceDomain, ComputeService::class.java)!!
-                service.replay(timeSource, vms, 0L, interference = true)
-            }
-        }
-}
diff --git a/opendc-experiments/opendc-experiments-scenario/src/jmh/resources/log4j2.xml b/opendc-experiments/opendc-experiments-scenario/src/jmh/resources/log4j2.xml
deleted file mode 100644
index c496dd757..000000000
--- a/opendc-experiments/opendc-experiments-scenario/src/jmh/resources/log4j2.xml
+++ /dev/null
@@ -1,37 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<!--
-  ~ MIT License
-  ~
-  ~ Copyright (c) 2020 atlarge-research
-  ~
-  ~ Permission is hereby granted, free of charge, to any person obtaining a copy
-  ~ of this software and associated documentation files (the "Software"), to deal
-  ~ in the Software without restriction, including without limitation the rights
-  ~ to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-  ~ copies of the Software, and to permit persons to whom the Software is
-  ~ furnished to do so, subject to the following conditions:
-  ~
-  ~ The above copyright notice and this permission notice shall be included in all
-  ~ copies or substantial portions of the Software.
-  ~
-  ~ THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-  ~ IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-  ~ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-  ~ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-  ~ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-  ~ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-  ~ SOFTWARE.
-  -->
-
-<Configuration status="WARN">
-    <Appenders>
-        <Console name="Console" target="SYSTEM_OUT">
-            <PatternLayout pattern="%d{HH:mm:ss.SSS} [%highlight{%-5level}] %logger{36} - %msg%n" disableAnsi="false"/>
-        </Console>
-    </Appenders>
-    <Loggers>
-        <Root level="warn">
-            <AppenderRef ref="Console"/>
-        </Root>
-    </Loggers>
-</Configuration>
diff --git a/opendc-experiments/opendc-experiments-scenario/src/jmh/resources/topology.txt b/opendc-experiments/opendc-experiments-scenario/src/jmh/resources/topology.txt
deleted file mode 100644
index 6b347bff4..000000000
--- a/opendc-experiments/opendc-experiments-scenario/src/jmh/resources/topology.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-ClusterID;ClusterName;Cores;Speed;Memory;numberOfHosts;memoryCapacityPerHost;coreCountPerHost
-A01;A01;32;3.2;2048;1;256;32
-B01;B01;48;2.93;1256;6;64;8
-C01;C01;32;3.2;2048;2;128;16
-
diff --git a/opendc-experiments/opendc-experiments-scenario/src/main/Python_scripts/.ipynb_checkpoints/OpenDCdemo-checkpoint.ipynb b/opendc-experiments/opendc-experiments-scenario/src/main/Python_scripts/.ipynb_checkpoints/OpenDCdemo-checkpoint.ipynb
deleted file mode 100644
index 792e5995d..000000000
--- a/opendc-experiments/opendc-experiments-scenario/src/main/Python_scripts/.ipynb_checkpoints/OpenDCdemo-checkpoint.ipynb
+++ /dev/null
@@ -1,1379 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "18170001",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "from IPython.display import display, HTML\n",
-    "\n",
-    "base_folder = \"../../../../..\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "422f4d05",
-   "metadata": {},
-   "source": [
-    "## Topologies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "a2d05361",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Topology name: multi\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ClusterID</th>\n",
-       "      <th>ClusterName</th>\n",
-       "      <th>Cores</th>\n",
-       "      <th>Speed</th>\n",
-       "      <th>Memory</th>\n",
-       "      <th>numberOfHosts</th>\n",
-       "      <th>memoryCapacityPerHost</th>\n",
-       "      <th>coreCountPerHost</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>A01</td>\n",
-       "      <td>A01</td>\n",
-       "      <td>32</td>\n",
-       "      <td>3.20</td>\n",
-       "      <td>2048</td>\n",
-       "      <td>1</td>\n",
-       "      <td>256</td>\n",
-       "      <td>32</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>B01</td>\n",
-       "      <td>B01</td>\n",
-       "      <td>48</td>\n",
-       "      <td>2.93</td>\n",
-       "      <td>1256</td>\n",
-       "      <td>6</td>\n",
-       "      <td>64</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>C01</td>\n",
-       "      <td>C01</td>\n",
-       "      <td>32</td>\n",
-       "      <td>3.20</td>\n",
-       "      <td>2048</td>\n",
-       "      <td>2</td>\n",
-       "      <td>128</td>\n",
-       "      <td>16</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Topology name: single\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ClusterID</th>\n",
-       "      <th>ClusterName</th>\n",
-       "      <th>Cores</th>\n",
-       "      <th>Speed</th>\n",
-       "      <th>Memory</th>\n",
-       "      <th>numberOfHosts</th>\n",
-       "      <th>memoryCapacityPerHost</th>\n",
-       "      <th>coreCountPerHost</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>A01</td>\n",
-       "      <td>A01</td>\n",
-       "      <td>8</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>128</td>\n",
-       "      <td>1</td>\n",
-       "      <td>128</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def read_topology(topology_name):\n",
-    "    print(f\"Topology name: {topology_name}\")\n",
-    "    df = pd.read_csv(f\"{base_folder}/resources/env/{topology_name}.txt\", delimiter=\";\")\n",
-    "    display(HTML(df.to_html()))\n",
-    "        \n",
-    "read_topology(\"multi\")\n",
-    "read_topology(\"single\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8f4fe54d",
-   "metadata": {},
-   "source": [
-    "## Traces"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "fd17d88a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>timestamp</th>\n",
-       "      <th>duration</th>\n",
-       "      <th>cpu_count</th>\n",
-       "      <th>cpu_usage</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1019</td>\n",
-       "      <td>2013-08-12 13:40:46+00:00</td>\n",
-       "      <td>300000</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24015</th>\n",
-       "      <td>1129</td>\n",
-       "      <td>2013-08-12 13:40:46+00:00</td>\n",
-       "      <td>300000</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3.901332</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34039</th>\n",
-       "      <td>1138</td>\n",
-       "      <td>2013-08-12 13:40:46+00:00</td>\n",
-       "      <td>300000</td>\n",
-       "      <td>1</td>\n",
-       "      <td>20.799994</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>41348</th>\n",
-       "      <td>1147</td>\n",
-       "      <td>2013-08-12 13:40:46+00:00</td>\n",
-       "      <td>300000</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>47515</th>\n",
-       "      <td>1152</td>\n",
-       "      <td>2013-08-12 13:40:46+00:00</td>\n",
-       "      <td>300000</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>53661</th>\n",
-       "      <td>116</td>\n",
-       "      <td>2013-08-12 13:40:46+00:00</td>\n",
-       "      <td>300000</td>\n",
-       "      <td>4</td>\n",
-       "      <td>158.003968</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>61559</th>\n",
-       "      <td>141</td>\n",
-       "      <td>2013-08-12 13:40:46+00:00</td>\n",
-       "      <td>300000</td>\n",
-       "      <td>2</td>\n",
-       "      <td>56.569320</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>66380</th>\n",
-       "      <td>190</td>\n",
-       "      <td>2013-08-12 13:40:46+00:00</td>\n",
-       "      <td>300000</td>\n",
-       "      <td>8</td>\n",
-       "      <td>14693.462756</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>73300</th>\n",
-       "      <td>205</td>\n",
-       "      <td>2013-08-12 13:40:46+00:00</td>\n",
-       "      <td>300000</td>\n",
-       "      <td>8</td>\n",
-       "      <td>16185.864295</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>87698</th>\n",
-       "      <td>244</td>\n",
-       "      <td>2013-08-12 13:40:46+00:00</td>\n",
-       "      <td>300000</td>\n",
-       "      <td>8</td>\n",
-       "      <td>95.333296</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>95284</th>\n",
-       "      <td>272</td>\n",
-       "      <td>2013-08-12 13:40:46+00:00</td>\n",
-       "      <td>300000</td>\n",
-       "      <td>8</td>\n",
-       "      <td>164.666623</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>112192</th>\n",
-       "      <td>323</td>\n",
-       "      <td>2013-08-12 13:40:46+00:00</td>\n",
-       "      <td>300000</td>\n",
-       "      <td>2</td>\n",
-       "      <td>152.533273</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>125958</th>\n",
-       "      <td>378</td>\n",
-       "      <td>2013-08-12 13:40:46+00:00</td>\n",
-       "      <td>300000</td>\n",
-       "      <td>2</td>\n",
-       "      <td>76.266647</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>132745</th>\n",
-       "      <td>379</td>\n",
-       "      <td>2013-08-12 13:40:46+00:00</td>\n",
-       "      <td>300000</td>\n",
-       "      <td>2</td>\n",
-       "      <td>109.199970</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>139492</th>\n",
-       "      <td>449</td>\n",
-       "      <td>2013-08-12 13:40:46+00:00</td>\n",
-       "      <td>300000</td>\n",
-       "      <td>4</td>\n",
-       "      <td>154.266626</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>146840</th>\n",
-       "      <td>463</td>\n",
-       "      <td>2013-08-12 13:40:46+00:00</td>\n",
-       "      <td>300000</td>\n",
-       "      <td>4</td>\n",
-       "      <td>131.733298</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>153699</th>\n",
-       "      <td>466</td>\n",
-       "      <td>2013-08-12 13:40:46+00:00</td>\n",
-       "      <td>300000</td>\n",
-       "      <td>4</td>\n",
-       "      <td>131.733300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>160537</th>\n",
-       "      <td>467</td>\n",
-       "      <td>2013-08-12 13:40:46+00:00</td>\n",
-       "      <td>300000</td>\n",
-       "      <td>4</td>\n",
-       "      <td>185.466617</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>167694</th>\n",
-       "      <td>501</td>\n",
-       "      <td>2013-08-12 13:40:46+00:00</td>\n",
-       "      <td>300000</td>\n",
-       "      <td>4</td>\n",
-       "      <td>157.733310</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>174746</th>\n",
-       "      <td>506</td>\n",
-       "      <td>2013-08-12 13:40:46+00:00</td>\n",
-       "      <td>300000</td>\n",
-       "      <td>4</td>\n",
-       "      <td>183.733284</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>182064</th>\n",
-       "      <td>550</td>\n",
-       "      <td>2013-08-12 13:40:46+00:00</td>\n",
-       "      <td>300000</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1.733333</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>196282</th>\n",
-       "      <td>607</td>\n",
-       "      <td>2013-08-12 13:40:46+00:00</td>\n",
-       "      <td>300000</td>\n",
-       "      <td>1</td>\n",
-       "      <td>247.866604</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>203796</th>\n",
-       "      <td>626</td>\n",
-       "      <td>2013-08-12 13:40:46+00:00</td>\n",
-       "      <td>300000</td>\n",
-       "      <td>4</td>\n",
-       "      <td>10718.931688</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>206960</th>\n",
-       "      <td>636</td>\n",
-       "      <td>2013-08-12 13:40:46+00:00</td>\n",
-       "      <td>300000</td>\n",
-       "      <td>4</td>\n",
-       "      <td>10781.331724</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>209842</th>\n",
-       "      <td>677</td>\n",
-       "      <td>2013-08-12 13:40:46+00:00</td>\n",
-       "      <td>300000</td>\n",
-       "      <td>2</td>\n",
-       "      <td>181.999951</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>225939</th>\n",
-       "      <td>740</td>\n",
-       "      <td>2013-08-12 13:40:46+00:00</td>\n",
-       "      <td>300000</td>\n",
-       "      <td>32</td>\n",
-       "      <td>2048.399624</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>234340</th>\n",
-       "      <td>750</td>\n",
-       "      <td>2013-08-12 13:40:46+00:00</td>\n",
-       "      <td>300000</td>\n",
-       "      <td>8</td>\n",
-       "      <td>145.599961</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>252091</th>\n",
-       "      <td>851</td>\n",
-       "      <td>2013-08-12 13:40:46+00:00</td>\n",
-       "      <td>300000</td>\n",
-       "      <td>2</td>\n",
-       "      <td>29.259993</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>256754</th>\n",
-       "      <td>871</td>\n",
-       "      <td>2013-08-12 13:40:46+00:00</td>\n",
-       "      <td>300000</td>\n",
-       "      <td>4</td>\n",
-       "      <td>158.003974</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>265560</th>\n",
-       "      <td>957</td>\n",
-       "      <td>2013-08-12 13:40:46+00:00</td>\n",
-       "      <td>300000</td>\n",
-       "      <td>4</td>\n",
-       "      <td>128.266613</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>268345</th>\n",
-       "      <td>997</td>\n",
-       "      <td>2013-08-12 13:40:46+00:00</td>\n",
-       "      <td>300000</td>\n",
-       "      <td>8</td>\n",
-       "      <td>10235.198844</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          id                 timestamp  duration  cpu_count     cpu_usage\n",
-       "0       1019 2013-08-12 13:40:46+00:00    300000          1      0.000000\n",
-       "24015   1129 2013-08-12 13:40:46+00:00    300000          1      3.901332\n",
-       "34039   1138 2013-08-12 13:40:46+00:00    300000          1     20.799994\n",
-       "41348   1147 2013-08-12 13:40:46+00:00    300000          1      0.000000\n",
-       "47515   1152 2013-08-12 13:40:46+00:00    300000          1      0.000000\n",
-       "53661    116 2013-08-12 13:40:46+00:00    300000          4    158.003968\n",
-       "61559    141 2013-08-12 13:40:46+00:00    300000          2     56.569320\n",
-       "66380    190 2013-08-12 13:40:46+00:00    300000          8  14693.462756\n",
-       "73300    205 2013-08-12 13:40:46+00:00    300000          8  16185.864295\n",
-       "87698    244 2013-08-12 13:40:46+00:00    300000          8     95.333296\n",
-       "95284    272 2013-08-12 13:40:46+00:00    300000          8    164.666623\n",
-       "112192   323 2013-08-12 13:40:46+00:00    300000          2    152.533273\n",
-       "125958   378 2013-08-12 13:40:46+00:00    300000          2     76.266647\n",
-       "132745   379 2013-08-12 13:40:46+00:00    300000          2    109.199970\n",
-       "139492   449 2013-08-12 13:40:46+00:00    300000          4    154.266626\n",
-       "146840   463 2013-08-12 13:40:46+00:00    300000          4    131.733298\n",
-       "153699   466 2013-08-12 13:40:46+00:00    300000          4    131.733300\n",
-       "160537   467 2013-08-12 13:40:46+00:00    300000          4    185.466617\n",
-       "167694   501 2013-08-12 13:40:46+00:00    300000          4    157.733310\n",
-       "174746   506 2013-08-12 13:40:46+00:00    300000          4    183.733284\n",
-       "182064   550 2013-08-12 13:40:46+00:00    300000          1      1.733333\n",
-       "196282   607 2013-08-12 13:40:46+00:00    300000          1    247.866604\n",
-       "203796   626 2013-08-12 13:40:46+00:00    300000          4  10718.931688\n",
-       "206960   636 2013-08-12 13:40:46+00:00    300000          4  10781.331724\n",
-       "209842   677 2013-08-12 13:40:46+00:00    300000          2    181.999951\n",
-       "225939   740 2013-08-12 13:40:46+00:00    300000         32   2048.399624\n",
-       "234340   750 2013-08-12 13:40:46+00:00    300000          8    145.599961\n",
-       "252091   851 2013-08-12 13:40:46+00:00    300000          2     29.259993\n",
-       "256754   871 2013-08-12 13:40:46+00:00    300000          4    158.003974\n",
-       "265560   957 2013-08-12 13:40:46+00:00    300000          4    128.266613\n",
-       "268345   997 2013-08-12 13:40:46+00:00    300000          8  10235.198844"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_trace = pd.read_parquet(f\"{base_folder}/resources/bitbrains-small/trace/trace.parquet\")\n",
-    "df_trace[df_trace[\"timestamp\"] == df_trace[\"timestamp\"].min()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "346f097f",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>start_time</th>\n",
-       "      <th>stop_time</th>\n",
-       "      <th>cpu_count</th>\n",
-       "      <th>cpu_capacity</th>\n",
-       "      <th>mem_capacity</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1019</td>\n",
-       "      <td>2013-08-12 13:35:46+00:00</td>\n",
-       "      <td>2013-09-11 13:39:58+00:00</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2926.000135</td>\n",
-       "      <td>181352</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1023</td>\n",
-       "      <td>2013-08-12 13:35:46+00:00</td>\n",
-       "      <td>2013-09-11 13:39:58+00:00</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2925.999560</td>\n",
-       "      <td>260096</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1026</td>\n",
-       "      <td>2013-08-12 13:35:46+00:00</td>\n",
-       "      <td>2013-09-11 13:39:58+00:00</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2925.999717</td>\n",
-       "      <td>249972</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1052</td>\n",
-       "      <td>2013-08-29 14:38:12+00:00</td>\n",
-       "      <td>2013-09-05 07:09:07+00:00</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2926.000107</td>\n",
-       "      <td>131245</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1073</td>\n",
-       "      <td>2013-08-21 11:07:12+00:00</td>\n",
-       "      <td>2013-09-11 13:39:58+00:00</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2599.999649</td>\n",
-       "      <td>179306</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     id                start_time                 stop_time  cpu_count  \\\n",
-       "0  1019 2013-08-12 13:35:46+00:00 2013-09-11 13:39:58+00:00          1   \n",
-       "1  1023 2013-08-12 13:35:46+00:00 2013-09-11 13:39:58+00:00          1   \n",
-       "2  1026 2013-08-12 13:35:46+00:00 2013-09-11 13:39:58+00:00          1   \n",
-       "3  1052 2013-08-29 14:38:12+00:00 2013-09-05 07:09:07+00:00          1   \n",
-       "4  1073 2013-08-21 11:07:12+00:00 2013-09-11 13:39:58+00:00          1   \n",
-       "\n",
-       "   cpu_capacity  mem_capacity  \n",
-       "0   2926.000135        181352  \n",
-       "1   2925.999560        260096  \n",
-       "2   2925.999717        249972  \n",
-       "3   2926.000107        131245  \n",
-       "4   2599.999649        179306  "
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_meta = pd.read_parquet(f\"{base_folder}/resources/bitbrains-small/trace/meta.parquet\")\n",
-    "df_meta.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "13bf9fdb",
-   "metadata": {},
-   "source": [
-    "# Lets run this in OpenDC!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c9766446",
-   "metadata": {},
-   "source": [
-    "## Resulting Files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "id": "0d400ffd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output_folder = f\"{base_folder}/output\"\n",
-    "workload = \"workload=bitbrains-small\"\n",
-    "seed = \"seed=0\"\n",
-    "\n",
-    "df_host_single = pd.read_parquet(f\"{output_folder}/host/topology=single/{workload}/{seed}/data.parquet\")\n",
-    "df_host_multi = pd.read_parquet(f\"{output_folder}/host/topology=multi/{workload}/{seed}/data.parquet\")\n",
-    "\n",
-    "df_server_single = pd.read_parquet(f\"{output_folder}/server/topology=single/{workload}/{seed}/data.parquet\")\n",
-    "df_server_multi = pd.read_parquet(f\"{output_folder}/server/topology=multi/{workload}/{seed}/data.parquet\")\n",
-    "\n",
-    "df_service_single = pd.read_parquet(f\"{output_folder}/service/topology=single/{workload}/{seed}/data.parquet\")\n",
-    "df_service_multi = pd.read_parquet(f\"{output_folder}/service/topology=multi/{workload}/{seed}/data.parquet\")\n",
-    "\n",
-    "def add_absolute_timestamp(df, start_dt):\n",
-    "    df[\"absolute_timestamp\"] = start_dt + (df[\"timestamp\"] - df[\"timestamp\"].min())\n",
-    "\n",
-    "add_absolute_timestamp(df_host_single, df_meta[\"start_time\"].min())\n",
-    "add_absolute_timestamp(df_host_single, df_meta[\"start_time\"].min())\n",
-    "\n",
-    "add_absolute_timestamp(df_server_single, df_meta[\"start_time\"].min())\n",
-    "add_absolute_timestamp(df_server_multi, df_meta[\"start_time\"].min())\n",
-    "\n",
-    "add_absolute_timestamp(df_service_single, df_meta[\"start_time\"].min())\n",
-    "add_absolute_timestamp(df_service_multi, df_meta[\"start_time\"].min())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "id": "a9a61332",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1080"
-      ]
-     },
-     "execution_count": 90,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(df_service_single)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "id": "e1d01f85",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1970-01-01 02:00:00+00:00    1\n",
-       "1970-03-02 12:00:00+00:00    1\n",
-       "1970-03-01 08:00:00+00:00    1\n",
-       "1970-03-01 10:00:00+00:00    1\n",
-       "1970-03-01 12:00:00+00:00    1\n",
-       "                            ..\n",
-       "1970-01-31 12:00:00+00:00    1\n",
-       "1970-01-31 14:00:00+00:00    1\n",
-       "1970-01-31 16:00:00+00:00    1\n",
-       "1970-01-31 18:00:00+00:00    1\n",
-       "1970-04-01 00:00:00+00:00    1\n",
-       "Name: timestamp, Length: 1080, dtype: int64"
-      ]
-     },
-     "execution_count": 91,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_host_single.timestamp.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "id": "5b36f79c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([50, 49, 47, 46, 45, 44, 34, 16, 14, 13, 12, 11, 10])"
-      ]
-     },
-     "execution_count": 103,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_server_single.timestamp.value_counts().unique()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "id": "699268f3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1970-01-01 02:00:00+00:00    1\n",
-       "1970-03-02 12:00:00+00:00    1\n",
-       "1970-03-01 08:00:00+00:00    1\n",
-       "1970-03-01 10:00:00+00:00    1\n",
-       "1970-03-01 12:00:00+00:00    1\n",
-       "                            ..\n",
-       "1970-01-31 12:00:00+00:00    1\n",
-       "1970-01-31 14:00:00+00:00    1\n",
-       "1970-01-31 16:00:00+00:00    1\n",
-       "1970-01-31 18:00:00+00:00    1\n",
-       "1970-04-01 00:00:00+00:00    1\n",
-       "Name: timestamp, Length: 1080, dtype: int64"
-      ]
-     },
-     "execution_count": 93,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_service_single.timestamp.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "id": "a32f9d66",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([44, 45, 46, 47, 49, 50, 34, 16, 14, 13, 12, 11, 10], dtype=int32)"
-      ]
-     },
-     "execution_count": 104,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(df_service_single.servers_active + df_service_single.servers_pending).unique() "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "id": "fe5cc9c0",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 102,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "set(d1) == set(d2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "09d31c91",
-   "metadata": {},
-   "source": [
-    "## Power Usage"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "82f0a24a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "single topology:  2227322672.880138\n",
-      "multi topology:   5865185988.6738405\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"single topology:  {df_host_single.power_total.sum()}\")\n",
-    "print(f\"multi topology:   {df_host_multi.power_total.sum()}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7ab3357d",
-   "metadata": {},
-   "source": [
-    "## CPU usage"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "id": "e94db3a6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "single topology:  0.575979511557793\n",
-      "multi topology:   0.34249306908883387\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"single topology:  {df_host_single.cpu_utilization.mean()}\")\n",
-    "print(f\"multi topology:   {df_host_multi.cpu_utilization.mean()}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e000a260",
-   "metadata": {},
-   "source": [
-    "## CPU utilization"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "id": "8d7daa45",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "single topology:  0.575979511557793\n",
-      "multi topology:   0.34249306908883387\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"single topology:  {df_host_single.cpu_utilization.mean()}\")\n",
-    "print(f\"multi topology:   {df_host_multi.cpu_utilization.mean()}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ad97741c",
-   "metadata": {},
-   "source": [
-    "## Plotting Results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "id": "5df8f9aa",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "data = df_host_multi.cpu_utilization\n",
-    "plt.hist(data, weights=np.ones_like(data) / len(data),\n",
-    "         alpha=0.7, label=\"multi\", bins=30)\n",
-    "\n",
-    "\n",
-    "data = df_host_single.cpu_utilization\n",
-    "plt.hist(data, weights=np.ones_like(data) / len(data),\n",
-    "         alpha=0.7, label=\"single\", bins=30)\n",
-    "\n",
-    "plt.xlabel(\"CPU utilization\")\n",
-    "plt.ylabel(\"Frequency\")\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 105,
-   "id": "42c0c638",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f6fc2d09510>"
-      ]
-     },
-     "execution_count": 105,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "plt.plot(df_service_single.servers_pending, label=\"servers pending\")\n",
-    "plt.plot(df_service_single.servers_active, label=\"servers active\")\n",
-    "\n",
-    "plt.legend()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 106,
-   "id": "1a688c2d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f6fc2cc7ca0>"
-      ]
-     },
-     "execution_count": 106,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "plt.plot(df_service_multi.servers_pending, label=\"servers pending\")\n",
-    "plt.plot(df_service_multi.servers_active, label=\"servers active\")\n",
-    "\n",
-    "plt.legend()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 117,
-   "id": "dc4e17cd",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>timestamp</th>\n",
-       "      <th>host_id</th>\n",
-       "      <th>cpu_count</th>\n",
-       "      <th>mem_capacity</th>\n",
-       "      <th>guests_terminated</th>\n",
-       "      <th>guests_running</th>\n",
-       "      <th>guests_error</th>\n",
-       "      <th>guests_invalid</th>\n",
-       "      <th>cpu_limit</th>\n",
-       "      <th>cpu_usage</th>\n",
-       "      <th>...</th>\n",
-       "      <th>cpu_utilization</th>\n",
-       "      <th>cpu_time_active</th>\n",
-       "      <th>cpu_time_idle</th>\n",
-       "      <th>cpu_time_steal</th>\n",
-       "      <th>cpu_time_lost</th>\n",
-       "      <th>power_total</th>\n",
-       "      <th>uptime</th>\n",
-       "      <th>downtime</th>\n",
-       "      <th>boot_time</th>\n",
-       "      <th>absolute_timestamp</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1970-01-01 02:00:00+00:00</td>\n",
-       "      <td>b'\\xe2 \\xa89{\\x1d\\xcd\\xaf\\x00\\x00\\x00\\x00\\x00\\...</td>\n",
-       "      <td>8</td>\n",
-       "      <td>128000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>15</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>25600.0</td>\n",
-       "      <td>9446.762695</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.369014</td>\n",
-       "      <td>13838</td>\n",
-       "      <td>43761</td>\n",
-       "      <td>1008</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.699475e+06</td>\n",
-       "      <td>7200000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1970-01-01 00:00:00+00:00</td>\n",
-       "      <td>2013-08-12 13:35:46+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1970-01-01 04:00:00+00:00</td>\n",
-       "      <td>b'\\xe2 \\xa89{\\x1d\\xcd\\xaf\\x00\\x00\\x00\\x00\\x00\\...</td>\n",
-       "      <td>8</td>\n",
-       "      <td>128000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>15</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>25600.0</td>\n",
-       "      <td>25600.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>57592</td>\n",
-       "      <td>8</td>\n",
-       "      <td>73720</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.519850e+06</td>\n",
-       "      <td>7200000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1970-01-01 00:00:00+00:00</td>\n",
-       "      <td>2013-08-12 15:35:46+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1970-01-01 06:00:00+00:00</td>\n",
-       "      <td>b'\\xe2 \\xa89{\\x1d\\xcd\\xaf\\x00\\x00\\x00\\x00\\x00\\...</td>\n",
-       "      <td>8</td>\n",
-       "      <td>128000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>15</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>25600.0</td>\n",
-       "      <td>25600.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>57600</td>\n",
-       "      <td>0</td>\n",
-       "      <td>81518</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.520000e+06</td>\n",
-       "      <td>7200000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1970-01-01 00:00:00+00:00</td>\n",
-       "      <td>2013-08-12 17:35:46+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1970-01-01 08:00:00+00:00</td>\n",
-       "      <td>b'\\xe2 \\xa89{\\x1d\\xcd\\xaf\\x00\\x00\\x00\\x00\\x00\\...</td>\n",
-       "      <td>8</td>\n",
-       "      <td>128000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>15</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>25600.0</td>\n",
-       "      <td>25600.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>57592</td>\n",
-       "      <td>8</td>\n",
-       "      <td>73134</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.519850e+06</td>\n",
-       "      <td>7200000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1970-01-01 00:00:00+00:00</td>\n",
-       "      <td>2013-08-12 19:35:46+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1970-01-01 10:00:00+00:00</td>\n",
-       "      <td>b'\\xe2 \\xa89{\\x1d\\xcd\\xaf\\x00\\x00\\x00\\x00\\x00\\...</td>\n",
-       "      <td>8</td>\n",
-       "      <td>128000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>15</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>25600.0</td>\n",
-       "      <td>25600.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>57592</td>\n",
-       "      <td>8</td>\n",
-       "      <td>74832</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.519850e+06</td>\n",
-       "      <td>7200000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1970-01-01 00:00:00+00:00</td>\n",
-       "      <td>2013-08-12 21:35:46+00:00</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 21 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                  timestamp  \\\n",
-       "0 1970-01-01 02:00:00+00:00   \n",
-       "1 1970-01-01 04:00:00+00:00   \n",
-       "2 1970-01-01 06:00:00+00:00   \n",
-       "3 1970-01-01 08:00:00+00:00   \n",
-       "4 1970-01-01 10:00:00+00:00   \n",
-       "\n",
-       "                                             host_id  cpu_count  mem_capacity  \\\n",
-       "0  b'\\xe2 \\xa89{\\x1d\\xcd\\xaf\\x00\\x00\\x00\\x00\\x00\\...          8        128000   \n",
-       "1  b'\\xe2 \\xa89{\\x1d\\xcd\\xaf\\x00\\x00\\x00\\x00\\x00\\...          8        128000   \n",
-       "2  b'\\xe2 \\xa89{\\x1d\\xcd\\xaf\\x00\\x00\\x00\\x00\\x00\\...          8        128000   \n",
-       "3  b'\\xe2 \\xa89{\\x1d\\xcd\\xaf\\x00\\x00\\x00\\x00\\x00\\...          8        128000   \n",
-       "4  b'\\xe2 \\xa89{\\x1d\\xcd\\xaf\\x00\\x00\\x00\\x00\\x00\\...          8        128000   \n",
-       "\n",
-       "   guests_terminated  guests_running  guests_error  guests_invalid  cpu_limit  \\\n",
-       "0                  0              15             0               0    25600.0   \n",
-       "1                  0              15             0               0    25600.0   \n",
-       "2                  0              15             0               0    25600.0   \n",
-       "3                  0              15             0               0    25600.0   \n",
-       "4                  0              15             0               0    25600.0   \n",
-       "\n",
-       "      cpu_usage  ...  cpu_utilization  cpu_time_active  cpu_time_idle  \\\n",
-       "0   9446.762695  ...         0.369014            13838          43761   \n",
-       "1  25600.000000  ...         1.000000            57592              8   \n",
-       "2  25600.000000  ...         1.000000            57600              0   \n",
-       "3  25600.000000  ...         1.000000            57592              8   \n",
-       "4  25600.000000  ...         1.000000            57592              8   \n",
-       "\n",
-       "   cpu_time_steal  cpu_time_lost   power_total   uptime  downtime  \\\n",
-       "0            1008              0  1.699475e+06  7200000         0   \n",
-       "1           73720              0  2.519850e+06  7200000         0   \n",
-       "2           81518              0  2.520000e+06  7200000         0   \n",
-       "3           73134              0  2.519850e+06  7200000         0   \n",
-       "4           74832              0  2.519850e+06  7200000         0   \n",
-       "\n",
-       "                  boot_time        absolute_timestamp  \n",
-       "0 1970-01-01 00:00:00+00:00 2013-08-12 13:35:46+00:00  \n",
-       "1 1970-01-01 00:00:00+00:00 2013-08-12 15:35:46+00:00  \n",
-       "2 1970-01-01 00:00:00+00:00 2013-08-12 17:35:46+00:00  \n",
-       "3 1970-01-01 00:00:00+00:00 2013-08-12 19:35:46+00:00  \n",
-       "4 1970-01-01 00:00:00+00:00 2013-08-12 21:35:46+00:00  \n",
-       "\n",
-       "[5 rows x 21 columns]"
-      ]
-     },
-     "execution_count": 117,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_host_single.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 120,
-   "id": "b0e6c7bf",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[]"
-      ]
-     },
-     "execution_count": 120,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "utilization = df_host_single.cpu_utilization.to_numpy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "id": "aea7b79d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f6f870ccfa0>]"
-      ]
-     },
-     "execution_count": 122,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "window = 100\n",
-    "avg_utilization = []\n",
-    "\n",
-    "for ind in range(len(utilization) - window + 1):\n",
-    "    avg_utilization.append(np.mean(utilization[ind:ind+window]))\n",
-    "    \n",
-    "plt.plot(avg_utilization)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "575f824b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sum_util = []\n",
-    "\n",
-    "last_util = 0\n",
-    "for util in utilization:\n",
-    "    sum_util.append(util + last_util)\n",
-    "    last_util = sum_util[-1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "94d64f88",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fd9366b70d0>]"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(sum_util)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7962b16f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output_file = \"../Python_scripts/meta_small.parquet\"\n",
-    "output_file_path = Path(output_file)\n",
-    "\n",
-    "df_meta_new.to_parquet(output_file_path, index=False)\n",
-    "\n",
-    "output_file = \"../Python_scripts/trace_small.parquet\"\n",
-    "output_file_path = Path(output_file)\n",
-    "df_trace_new.to_parquet(output_file_path, index=False)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/opendc-experiments/opendc-experiments-scenario/src/main/Python_scripts/OpenDCdemo.ipynb b/opendc-experiments/opendc-experiments-scenario/src/main/Python_scripts/OpenDCdemo.ipynb
deleted file mode 100644
index 09ff26d6c..000000000
--- a/opendc-experiments/opendc-experiments-scenario/src/main/Python_scripts/OpenDCdemo.ipynb
+++ /dev/null
@@ -1,1121 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "18170001",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "from IPython.display import display, HTML\n",
-    "\n",
-    "base_folder = \"../\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "422f4d05",
-   "metadata": {},
-   "source": [
-    "## Topologies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "a2d05361",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Topology name: multi\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ClusterID</th>\n",
-       "      <th>ClusterName</th>\n",
-       "      <th>Cores</th>\n",
-       "      <th>Speed</th>\n",
-       "      <th>Memory</th>\n",
-       "      <th>numberOfHosts</th>\n",
-       "      <th>memoryCapacityPerHost</th>\n",
-       "      <th>coreCountPerHost</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>A01</td>\n",
-       "      <td>A01</td>\n",
-       "      <td>32</td>\n",
-       "      <td>3.20</td>\n",
-       "      <td>2048</td>\n",
-       "      <td>1</td>\n",
-       "      <td>256</td>\n",
-       "      <td>32</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>B01</td>\n",
-       "      <td>B01</td>\n",
-       "      <td>48</td>\n",
-       "      <td>2.93</td>\n",
-       "      <td>1256</td>\n",
-       "      <td>6</td>\n",
-       "      <td>64</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>C01</td>\n",
-       "      <td>C01</td>\n",
-       "      <td>32</td>\n",
-       "      <td>3.20</td>\n",
-       "      <td>2048</td>\n",
-       "      <td>2</td>\n",
-       "      <td>128</td>\n",
-       "      <td>16</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Topology name: single\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ClusterID</th>\n",
-       "      <th>ClusterName</th>\n",
-       "      <th>Cores</th>\n",
-       "      <th>Speed</th>\n",
-       "      <th>Memory</th>\n",
-       "      <th>numberOfHosts</th>\n",
-       "      <th>memoryCapacityPerHost</th>\n",
-       "      <th>coreCountPerHost</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>A01</td>\n",
-       "      <td>A01</td>\n",
-       "      <td>8</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>128</td>\n",
-       "      <td>1</td>\n",
-       "      <td>128</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def read_topology(topology_name):\n",
-    "    print(f\"Topology name: {topology_name}\")\n",
-    "    df = pd.read_csv(f\"{base_folder}/resources/env/{topology_name}.txt\", delimiter=\";\")\n",
-    "    display(HTML(df.to_html()))\n",
-    "        \n",
-    "read_topology(\"multi\")\n",
-    "read_topology(\"single\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8f4fe54d",
-   "metadata": {},
-   "source": [
-    "## Traces"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "fd17d88a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>timestamp</th>\n",
-       "      <th>duration</th>\n",
-       "      <th>cpu_count</th>\n",
-       "      <th>cpu_usage</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1019</td>\n",
-       "      <td>2013-08-12 13:40:46+00:00</td>\n",
-       "      <td>300000</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1019</td>\n",
-       "      <td>2013-08-12 13:45:46+00:00</td>\n",
-       "      <td>300000</td>\n",
-       "      <td>1</td>\n",
-       "      <td>11.703998</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1019</td>\n",
-       "      <td>2013-08-12 13:55:46+00:00</td>\n",
-       "      <td>600000</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1019</td>\n",
-       "      <td>2013-08-12 14:00:46+00:00</td>\n",
-       "      <td>300000</td>\n",
-       "      <td>1</td>\n",
-       "      <td>11.703998</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1019</td>\n",
-       "      <td>2013-08-12 14:15:46+00:00</td>\n",
-       "      <td>900000</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     id                 timestamp  duration  cpu_count  cpu_usage\n",
-       "0  1019 2013-08-12 13:40:46+00:00    300000          1   0.000000\n",
-       "1  1019 2013-08-12 13:45:46+00:00    300000          1  11.703998\n",
-       "2  1019 2013-08-12 13:55:46+00:00    600000          1   0.000000\n",
-       "3  1019 2013-08-12 14:00:46+00:00    300000          1  11.703998\n",
-       "4  1019 2013-08-12 14:15:46+00:00    900000          1   0.000000"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_trace = pd.read_parquet(f\"{base_folder}/resources/bitbrains-small/trace/trace.parquet\")\n",
-    "df_trace.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "346f097f",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>start_time</th>\n",
-       "      <th>stop_time</th>\n",
-       "      <th>cpu_count</th>\n",
-       "      <th>cpu_capacity</th>\n",
-       "      <th>mem_capacity</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1019</td>\n",
-       "      <td>2013-08-12 13:35:46+00:00</td>\n",
-       "      <td>2013-09-11 13:39:58+00:00</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2926.000135</td>\n",
-       "      <td>181352</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1023</td>\n",
-       "      <td>2013-08-12 13:35:46+00:00</td>\n",
-       "      <td>2013-09-11 13:39:58+00:00</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2925.999560</td>\n",
-       "      <td>260096</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1026</td>\n",
-       "      <td>2013-08-12 13:35:46+00:00</td>\n",
-       "      <td>2013-09-11 13:39:58+00:00</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2925.999717</td>\n",
-       "      <td>249972</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1052</td>\n",
-       "      <td>2013-08-29 14:38:12+00:00</td>\n",
-       "      <td>2013-09-05 07:09:07+00:00</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2926.000107</td>\n",
-       "      <td>131245</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1073</td>\n",
-       "      <td>2013-08-21 11:07:12+00:00</td>\n",
-       "      <td>2013-09-11 13:39:58+00:00</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2599.999649</td>\n",
-       "      <td>179306</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     id                start_time                 stop_time  cpu_count  \\\n",
-       "0  1019 2013-08-12 13:35:46+00:00 2013-09-11 13:39:58+00:00          1   \n",
-       "1  1023 2013-08-12 13:35:46+00:00 2013-09-11 13:39:58+00:00          1   \n",
-       "2  1026 2013-08-12 13:35:46+00:00 2013-09-11 13:39:58+00:00          1   \n",
-       "3  1052 2013-08-29 14:38:12+00:00 2013-09-05 07:09:07+00:00          1   \n",
-       "4  1073 2013-08-21 11:07:12+00:00 2013-09-11 13:39:58+00:00          1   \n",
-       "\n",
-       "   cpu_capacity  mem_capacity  \n",
-       "0   2926.000135        181352  \n",
-       "1   2925.999560        260096  \n",
-       "2   2925.999717        249972  \n",
-       "3   2926.000107        131245  \n",
-       "4   2599.999649        179306  "
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_meta = pd.read_parquet(f\"{base_folder}/resources/bitbrains-small/trace/meta.parquet\")\n",
-    "df_meta.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "13bf9fdb",
-   "metadata": {},
-   "source": [
-    "# Lets run this in OpenDC!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c9766446",
-   "metadata": {},
-   "source": [
-    "## Resulting Files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "0d400ffd",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "Addition/subtraction of integers and integer-arrays with Timestamp is no longer supported.  Instead of adding/subtracting `n`, use `n * obj.freq`",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[8], line 17\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21madd_absolute_timestamp\u001b[39m(df, start_dt):\n\u001b[1;32m     15\u001b[0m     df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mabsolute_timestamp\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m start_dt \u001b[38;5;241m+\u001b[39m (df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtimestamp\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m-\u001b[39m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtimestamp\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mmin())\n\u001b[0;32m---> 17\u001b[0m \u001b[43madd_absolute_timestamp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_host_single\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_meta\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstart_time\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     18\u001b[0m add_absolute_timestamp(df_host_single, df_meta[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstart_time\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mmin())\n\u001b[1;32m     20\u001b[0m add_absolute_timestamp(df_server_single, df_meta[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstart_time\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mmin())\n",
-      "Cell \u001b[0;32mIn[8], line 15\u001b[0m, in \u001b[0;36madd_absolute_timestamp\u001b[0;34m(df, start_dt)\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21madd_absolute_timestamp\u001b[39m(df, start_dt):\n\u001b[0;32m---> 15\u001b[0m     df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mabsolute_timestamp\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mstart_dt\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtimestamp\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtimestamp\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/ops/common.py:72\u001b[0m, in \u001b[0;36m_unpack_zerodim_and_defer.<locals>.new_method\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     68\u001b[0m             \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m\n\u001b[1;32m     70\u001b[0m other \u001b[38;5;241m=\u001b[39m item_from_zerodim(other)\n\u001b[0;32m---> 72\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mother\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:107\u001b[0m, in \u001b[0;36mOpsMixin.__radd__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    105\u001b[0m \u001b[38;5;129m@unpack_zerodim_and_defer\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__radd__\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    106\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__radd__\u001b[39m(\u001b[38;5;28mself\u001b[39m, other):\n\u001b[0;32m--> 107\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_arith_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mother\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mroperator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mradd\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/series.py:6262\u001b[0m, in \u001b[0;36mSeries._arith_method\u001b[0;34m(self, other, op)\u001b[0m\n\u001b[1;32m   6260\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_arith_method\u001b[39m(\u001b[38;5;28mself\u001b[39m, other, op):\n\u001b[1;32m   6261\u001b[0m     \u001b[38;5;28mself\u001b[39m, other \u001b[38;5;241m=\u001b[39m ops\u001b[38;5;241m.\u001b[39malign_method_SERIES(\u001b[38;5;28mself\u001b[39m, other)\n\u001b[0;32m-> 6262\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mbase\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mIndexOpsMixin\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_arith_method\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mother\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/base.py:1325\u001b[0m, in \u001b[0;36mIndexOpsMixin._arith_method\u001b[0;34m(self, other, op)\u001b[0m\n\u001b[1;32m   1322\u001b[0m rvalues \u001b[38;5;241m=\u001b[39m ensure_wrapped_if_datetimelike(rvalues)\n\u001b[1;32m   1324\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m np\u001b[38;5;241m.\u001b[39merrstate(\u001b[38;5;28mall\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m-> 1325\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marithmetic_op\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1327\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_construct_result(result, name\u001b[38;5;241m=\u001b[39mres_name)\n",
-      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/ops/array_ops.py:218\u001b[0m, in \u001b[0;36marithmetic_op\u001b[0;34m(left, right, op)\u001b[0m\n\u001b[1;32m    205\u001b[0m \u001b[38;5;66;03m# NB: We assume that extract_array and ensure_wrapped_if_datetimelike\u001b[39;00m\n\u001b[1;32m    206\u001b[0m \u001b[38;5;66;03m#  have already been called on `left` and `right`,\u001b[39;00m\n\u001b[1;32m    207\u001b[0m \u001b[38;5;66;03m#  and `maybe_prepare_scalar_for_op` has already been called on `right`\u001b[39;00m\n\u001b[1;32m    208\u001b[0m \u001b[38;5;66;03m# We need to special-case datetime64/timedelta64 dtypes (e.g. because numpy\u001b[39;00m\n\u001b[1;32m    209\u001b[0m \u001b[38;5;66;03m# casts integer dtypes to timedelta64 when operating with timedelta64 - GH#22390)\u001b[39;00m\n\u001b[1;32m    211\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m    212\u001b[0m     should_extension_dispatch(left, right)\n\u001b[1;32m    213\u001b[0m     \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(right, (Timedelta, BaseOffset, Timestamp))\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    216\u001b[0m     \u001b[38;5;66;03m# Timedelta/Timestamp and other custom scalars are included in the check\u001b[39;00m\n\u001b[1;32m    217\u001b[0m     \u001b[38;5;66;03m# because numexpr will fail on it, see GH#31457\u001b[39;00m\n\u001b[0;32m--> 218\u001b[0m     res_values \u001b[38;5;241m=\u001b[39m \u001b[43mop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mleft\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mright\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    219\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    220\u001b[0m     \u001b[38;5;66;03m# TODO we should handle EAs consistently and move this check before the if/else\u001b[39;00m\n\u001b[1;32m    221\u001b[0m     \u001b[38;5;66;03m# (https://github.com/pandas-dev/pandas/issues/41165)\u001b[39;00m\n\u001b[1;32m    222\u001b[0m     _bool_arith_check(op, left, right)\n",
-      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/roperator.py:11\u001b[0m, in \u001b[0;36mradd\u001b[0;34m(left, right)\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mradd\u001b[39m(left, right):\n\u001b[0;32m---> 11\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mright\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mleft\u001b[49m\n",
-      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/_libs/tslibs/timestamps.pyx:504\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.timestamps._Timestamp.__add__\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: Addition/subtraction of integers and integer-arrays with Timestamp is no longer supported.  Instead of adding/subtracting `n`, use `n * obj.freq`"
-     ]
-    }
-   ],
-   "source": [
-    "output_folder = f\"{base_folder}/output\"\n",
-    "workload = \"workload=bitbrains-small\"\n",
-    "seed = \"seed=0\"\n",
-    "\n",
-    "df_host_single = pd.read_parquet(f\"{output_folder}/topology=single/{workload}/{seed}/host.parquet\")\n",
-    "df_host_multi = pd.read_parquet(f\"{output_folder}/topology=multi/{workload}/{seed}/host.parquet\")\n",
-    "\n",
-    "df_server_single = pd.read_parquet(f\"{output_folder}/topology=single/{workload}/{seed}/server.parquet\")\n",
-    "df_server_multi = pd.read_parquet(f\"{output_folder}/topology=multi/{workload}/{seed}/server.parquet\")\n",
-    "\n",
-    "df_service_single = pd.read_parquet(f\"{output_folder}/topology=single/{workload}/{seed}/service.parquet\")\n",
-    "df_service_multi = pd.read_parquet(f\"{output_folder}/topology=multi/{workload}/{seed}/service.parquet\")\n",
-    "\n",
-    "def add_absolute_timestamp(df, start_dt):\n",
-    "    df[\"absolute_timestamp\"] = start_dt + (df[\"timestamp\"] - df[\"timestamp\"].min())\n",
-    "\n",
-    "add_absolute_timestamp(df_host_single, df_meta[\"start_time\"].min())\n",
-    "add_absolute_timestamp(df_host_single, df_meta[\"start_time\"].min())\n",
-    "\n",
-    "add_absolute_timestamp(df_server_single, df_meta[\"start_time\"].min())\n",
-    "add_absolute_timestamp(df_server_multi, df_meta[\"start_time\"].min())\n",
-    "\n",
-    "add_absolute_timestamp(df_service_single, df_meta[\"start_time\"].min())\n",
-    "add_absolute_timestamp(df_service_multi, df_meta[\"start_time\"].min())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "a9a61332",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_host_single = pd.read_parquet(f\"{output_folder}/topology=single/{workload}/{seed}/host.parquet\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "d6fb41d9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Timedelta('0 days 00:05:00')"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.Timedelta(300000, unit=\"ms\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "3c271734",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "Can only use .dt accessor with datetimelike values",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[30], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdf_host_single\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtimestamp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdt\u001b[49m\u001b[38;5;241m.\u001b[39mto_pytimedelta()\n",
-      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/generic.py:5907\u001b[0m, in \u001b[0;36mNDFrame.__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5900\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m   5901\u001b[0m     name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_internal_names_set\n\u001b[1;32m   5902\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_metadata\n\u001b[1;32m   5903\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_accessors\n\u001b[1;32m   5904\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_info_axis\u001b[38;5;241m.\u001b[39m_can_hold_identifiers_and_holds_name(name)\n\u001b[1;32m   5905\u001b[0m ):\n\u001b[1;32m   5906\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m[name]\n\u001b[0;32m-> 5907\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mobject\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__getattribute__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/accessor.py:183\u001b[0m, in \u001b[0;36mCachedAccessor.__get__\u001b[0;34m(self, obj, cls)\u001b[0m\n\u001b[1;32m    180\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m obj \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    181\u001b[0m     \u001b[38;5;66;03m# we're accessing the attribute of the class, i.e., Dataset.geo\u001b[39;00m\n\u001b[1;32m    182\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_accessor\n\u001b[0;32m--> 183\u001b[0m accessor_obj \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_accessor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    184\u001b[0m \u001b[38;5;66;03m# Replace the property with the accessor object. Inspired by:\u001b[39;00m\n\u001b[1;32m    185\u001b[0m \u001b[38;5;66;03m# https://www.pydanny.com/cached-property.html\u001b[39;00m\n\u001b[1;32m    186\u001b[0m \u001b[38;5;66;03m# We need to use object.__setattr__ because we overwrite __setattr__ on\u001b[39;00m\n\u001b[1;32m    187\u001b[0m \u001b[38;5;66;03m# NDFrame\u001b[39;00m\n\u001b[1;32m    188\u001b[0m \u001b[38;5;28mobject\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__setattr__\u001b[39m(obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_name, accessor_obj)\n",
-      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/indexes/accessors.py:513\u001b[0m, in \u001b[0;36mCombinedDatetimelikeProperties.__new__\u001b[0;34m(cls, data)\u001b[0m\n\u001b[1;32m    510\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m is_period_dtype(data\u001b[38;5;241m.\u001b[39mdtype):\n\u001b[1;32m    511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m PeriodProperties(data, orig)\n\u001b[0;32m--> 513\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCan only use .dt accessor with datetimelike values\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "\u001b[0;31mAttributeError\u001b[0m: Can only use .dt accessor with datetimelike values"
-     ]
-    }
-   ],
-   "source": [
-    "df_host_single.timestamp.to_pytimedelta()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "89977c44",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0       1970-01-01 00:05:00+00:00\n",
-       "1       1970-01-01 00:10:00+00:00\n",
-       "2       1970-01-01 00:15:00+00:00\n",
-       "3       1970-01-01 00:20:00+00:00\n",
-       "4       1970-01-01 00:25:00+00:00\n",
-       "                   ...           \n",
-       "25918   1970-03-31 23:55:00+00:00\n",
-       "25919   1970-04-01 00:00:00+00:00\n",
-       "25920   1970-04-01 00:05:00+00:00\n",
-       "25921   1970-04-01 00:10:00+00:00\n",
-       "25922   1970-04-01 00:14:12+00:00\n",
-       "Name: timestamp, Length: 25923, dtype: datetime64[ns, UTC]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_host_single.timestamp"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 148,
-   "id": "eadd08e4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1970-01-01 00:05:00+00:00    1\n",
-       "1970-03-01 23:55:00+00:00    1\n",
-       "1970-03-02 00:45:00+00:00    1\n",
-       "1970-03-02 00:40:00+00:00    1\n",
-       "1970-03-02 00:35:00+00:00    1\n",
-       "                            ..\n",
-       "1970-01-30 23:50:00+00:00    1\n",
-       "1970-01-30 23:45:00+00:00    1\n",
-       "1970-01-30 23:40:00+00:00    1\n",
-       "1970-01-30 23:35:00+00:00    1\n",
-       "1970-04-01 00:10:00+00:00    1\n",
-       "Name: timestamp, Length: 25922, dtype: int64"
-      ]
-     },
-     "execution_count": 148,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_service_single.timestamp.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "id": "a32f9d66",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([44, 45, 46, 47, 49, 50, 34, 16, 14, 13, 12, 11, 10], dtype=int32)"
-      ]
-     },
-     "execution_count": 104,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(df_service_single.servers_active + df_service_single.servers_pending).unique() "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "id": "16f4a6b6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 102,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "set(d1) == set(d2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "09d31c91",
-   "metadata": {},
-   "source": [
-    "## Power Usage"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 150,
-   "id": "82f0a24a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "single topology:  2227253755.2781296\n",
-      "multi topology:   5864872551.731657\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"single topology:  {df_host_single.power_total.sum()}\")\n",
-    "print(f\"multi topology:   {df_host_multi.power_total.sum()}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7ab3357d",
-   "metadata": {},
-   "source": [
-    "## CPU usage"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 151,
-   "id": "e94db3a6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "single topology:  0.5760561514665646\n",
-      "multi topology:   0.3425398748402685\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"single topology:  {df_host_single.cpu_utilization.mean()}\")\n",
-    "print(f\"multi topology:   {df_host_multi.cpu_utilization.mean()}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e000a260",
-   "metadata": {},
-   "source": [
-    "## CPU utilization"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 152,
-   "id": "8d7daa45",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "single topology:  0.5760561514665646\n",
-      "multi topology:   0.3425398748402685\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"single topology:  {df_host_single.cpu_utilization.mean()}\")\n",
-    "print(f\"multi topology:   {df_host_multi.cpu_utilization.mean()}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ad97741c",
-   "metadata": {},
-   "source": [
-    "## Plotting Results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 153,
-   "id": "5df8f9aa",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "data = df_host_multi.cpu_utilization\n",
-    "plt.hist(data, weights=np.ones_like(data) / len(data),\n",
-    "         alpha=0.7, label=\"multi\", bins=30)\n",
-    "\n",
-    "\n",
-    "data = df_host_single.cpu_utilization\n",
-    "plt.hist(data, weights=np.ones_like(data) / len(data),\n",
-    "         alpha=0.7, label=\"single\", bins=30)\n",
-    "\n",
-    "plt.xlabel(\"CPU utilization\")\n",
-    "plt.ylabel(\"Frequency\")\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 154,
-   "id": "42c0c638",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f6fc2cc78b0>"
-      ]
-     },
-     "execution_count": 154,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "plt.plot(df_service_single.servers_pending, label=\"servers pending\")\n",
-    "plt.plot(df_service_single.servers_active, label=\"servers active\")\n",
-    "\n",
-    "plt.legend()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 155,
-   "id": "1a688c2d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f6fc02a5db0>"
-      ]
-     },
-     "execution_count": 155,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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PWVlZLk9VBgA0PIQPNCojRozQl19+6ekyAAAX4FVXu+AHlZWV8vf3r/N+KyoqFBAQUOf9WiU4ONi8IgUA0DAx8mGR1157TUlJSQoODlbz5s2VkpKikydPmvMXL16sxMREBQUFKSEhweVpwIcOHZLNZtPy5ct1ww03KCgoSAsXLlRwcLDee+89l/WsXLlS4eHhOnXqlCTpyJEjuvPOO+VwOBQZGamhQ4fq0KFDZvuxY8dq2LBhmjlzpmJjY9WhQwdJ0oIFC9S+fXsFBQUpOjpat99++3m3reZQxxtvvGEuk5qaqiNHjri0e/PNN9WlSxcFBQWpbdu2mj59uk6fPm3Ot9lsWrx4sW677TaFhISoffv2euutt1z6ePfdd3XVVVcpODhYN910k8u2/LiWGo8++qiuu+46/fWvf9UVV1yhiIgIjRw5UqWlpWab0tJSpaenKzQ0VC1bttTcuXN14403auLEiefdZgBA7Xl9+DAMQ6cqTnvkdbEPBC4oKNCoUaP0m9/8Rnv37tW6deuUlpZmLr906VJNnTpVM2fO1N69e/XEE0/okUce0csvv+zSz+TJkzVhwgTt3btXd9xxh4YMGaLs7GyXNkuXLtWwYcMUEhKiyspKpaamKjw8XBs3btTHH3+ssLAwDRw4UBUVFeYyOTk5ys/P15o1a7Rq1Spt27ZN999/v2bMmKH8/HytXr1affv2veA2njp1SjNnztRf/vIXffzxxyouLtbIkSPN+Rs3btSvf/1rTZgwQXv27NGLL76orKwszZw506Wf6dOn684779Rnn32mwYMHKz09XcePH5d0JkilpaXp1ltv1Y4dO3T33Xdr8uTJP/v579+/X2+88YZWrVqlVatWaf369Zo9e7Y5PzMzUx9//LHeeustrVmzRhs3btT27dt/tl8AQO14/WGX7yurdPXU9z2y7j0zUhUS8PMfYUFBgU6fPq20tDS1adNGkpSUlGTOnzZtmubMmaO0tDRJUnx8vPkFPWbMGLPdxIkTzTaSlJ6ertGjR+vUqVMKCQmR0+nUO++8o5UrV0qSli9frurqai1evNi8PHTJkiVyOBxat26dbr75ZklSaGioFi9ebB5uWbFihUJDQzVkyBCFh4erTZs26ty58wW3sbKyUs8//7x69uwpSXr55ZeVmJioLVu2qEePHpo+fbomT55sbk/btm312GOPadKkSZo2bZrZz9ixYzVq1ChJ0hNPPKF58+Zpy5YtGjhwoBYuXKgrr7xSc+bMkSR16NBBn3/+uf73f//3grVVV1crKytL4eHhkqTRo0crJydHM2fOVGlpqV5++WVlZ2erf//+5mcUGxt7wT4BALXn9SMf3qBTp07q37+/kpKSdMcdd2jRokX67rvvJEknT57U/v37NW7cOIWFhZmvxx9/XPv373fpp1u3bi7vBw8eLH9/f/PQxOuvvy673a6UlBRJ0s6dO7Vv3z6Fh4eb/UZGRqqsrMyl76SkJJfzPAYMGKA2bdqobdu2Gj16tJYuXWoexjkfPz8/de/e3XyfkJAgh8OhvXv3mrXMmDHDZRvvueceFRQUuPTdsWNH88+hoaGy2+3m7cz37t1rhpsaycnJF6xLkq644gozeEhnbote0+eBAwdUWVmpHj16mPMjIiLMw08AgLrn9SMfwf6+2jMj1WPrvhi+vr5as2aNPvnkE33wwQd67rnn9Mc//lG5ubnm3TIXLVp01hfrT2/dHRoa6vI+ICBAt99+u7KzszVy5EhlZ2drxIgR8vM7s1tPnDihrl27aunSpWfVdNlll5233/DwcG3fvl3r1q3TBx98oKlTp+rRRx/V1q1ba30Z64kTJzR9+nSXkZsaQUFB5p9/ehKtzWZTdXV1rdZZn30CAGrP68OHzWa7qEMfnmaz2dS7d2/17t1bU6dOVZs2bbRy5UplZmYqNjZWBw4cUHp6utv9pqena8CAAdq9e7fWrl2rxx9/3JzXpUsXLV++XFFRUbLb7W716+fnp5SUFKWkpGjatGlyOBxau3btOcODJJ0+fVrbtm0zRxDy8/NVXFysxMREs5b8/Hy1a9fO7W2skZiYeNYJqJs3b651f9KZwz/+/v7aunWr4uLiJEklJSX68ssvf/Y8FwBA7TT8b+1GIDc3Vzk5Obr55psVFRWl3NxcffPNN+YX8/Tp03X//fcrIiJCAwcOVHl5ubZt26bvvvtOmZmZF+y7b9++iomJUXp6uuLj411GT9LT0/XUU09p6NChmjFjhlq1aqV//vOfWrFihSZNmqRWrVqds89Vq1bpwIED6tu3r5o1a6Z3331X1dXVFzwU4e/vr/Hjx2vevHny8/PTfffdp169eplhZOrUqRoyZIji4uJ0++23y8fHRzt37tSuXbtcAtOF3HvvvZozZ44efPBB3X333crLy1NWVtZFLXs+4eHhGjNmjB588EFFRkYqKipK06ZNk4+PD7dRB4B6wjkfFrDb7dqwYYMGDx6sq666Sg8//LDmzJmjQYMGSZLuvvtuLV68WEuWLFFSUpJuuOEGZWVlKT4+/mf7ttlsGjVqlHbu3HnWyElISIg2bNiguLg4paWlKTExUePGjVNZWdkFR0IcDodWrFihfv36KTExUS+88IJeeeUVXXPNNeddJiQkRA899JB++ctfqnfv3goLC9Py5cvN+ampqVq1apU++OADde/eXb169dLcuXPNE3AvRlxcnF5//XW98cYb6tSpk1544QU98cQTF738+Tz99NNKTk7WkCFDlJKSot69e5uXPQMA6p7NuNjrRS3idDoVERGhkpKSs74gy8rKdPDgQcXHx/PF0IBkZWVp4sSJ5i3Ovd3Jkyd1+eWXa86cORo3btxZ8/k9BOC19q+V/nqbFH2t9LuP67TrC31//xSHXdDkffrpp/riiy/Uo0cPlZSUaMaMGZKkoUOHergyAKhrDeNwMuEDkPR///d/ys/PV0BAgLp27aqNGzeqRYsWni4LABolwgcu2dixYzV27FhPl1FrnTt3Vl5enqfLAIAmgxNOAQCApQgfAADAUoQPAABgKcIHAACwFOEDAABYivABAAAsRfhAgzV27FgNGzbM02UAAOoY9/mAxx06dEjx8fH69NNPdd1115nTn332WTWwu/8DQOPg4X9bCR9eqrKyUv7+/nXeb0VFhQICAuq839qIiIjwdAkAgHrAYReLvPbaa0pKSlJwcLCaN2+ulJQUnTx50py/ePFi80mqCQkJWrBggTnv0KFDstlsWr58uW644QYFBQVp4cKFCg4O1nvvveeynpUrVyo8PFynTp2SJB05ckR33nmnHA6HIiMjNXToUB06dMhsX3NoY+bMmYqNjVWHDh0kSQsWLFD79u0VFBSk6Oho3X777efdtm+//VajRo3S5ZdfrpCQECUlJemVV15xaVNdXa0nn3xS7dq1U2BgoOLi4jRz5kxJMp/e27lzZ9lsNt14440utUnSn/70J8XGxqq6utql36FDh+o3v/mN+f7NN99Uly5dFBQUpLZt22r69Ok6ffr0eWsHAFjP+0c+DEOqPOWZdfuHSLaff0hPQUGBRo0apSeffFK33XabSktLtXHjRvOQwtKlSzV16lQ9//zz6ty5sz799FPdc889Cg0N1ZgxY8x+Jk+erDlz5qhz584KCgrSxo0blZ2drUGDBpltli5dqmHDhikkJESVlZVKTU1VcnKyNm7cKD8/Pz3++OMaOHCgPvvsM3OEIycnR3a7XWvWrJEkbdu2Tffff7/++te/6he/+IWOHz+ujRs3nnf7ysrK1LVrVz300EOy2+165513NHr0aF155ZXq0aOHJGnKlClatGiR5s6dqz59+qigoEBffPGFJGnLli3q0aOH/vGPf+iaa64558jLHXfcofHjx+vDDz9U//79JUnHjx/X6tWr9e6770qSNm7cqF//+teaN2+err/+eu3fv1+//e1vJUnTpk372f0EALCG94ePylPSE7GeWff/HJUCQn+2WUFBgU6fPq20tDS1adNGkpSUlGTOnzZtmubMmaO0tDRJZ0YC9uzZoxdffNElfEycONFsI0np6ekaPXq0Tp06pZCQEDmdTr3zzjtauXKlJGn58uWqrq7W4sWLZftPSFqyZIkcDofWrVunm2++WZIUGhqqxYsXm1/6K1asUGhoqIYMGaLw8HC1adNGnTt3Pu/2XX755frDH/5gvh8/frzef/99/f3vf1ePHj1UWlqqZ599Vs8//7y5PVdeeaX69OkjSbrsssskSc2bN1dMTMw519GsWTMNGjRI2dnZZvh47bXX1KJFC910002SpOnTp2vy5MnmOtq2bavHHntMkyZNInwAQAPCYRcLdOrUSf3791dSUpLuuOMOLVq0SN99950k6eTJk9q/f7/GjRunsLAw8/X4449r//79Lv1069bN5f3gwYPl7++vt956S5L0+uuvy263KyUlRZK0c+dO7du3T+Hh4Wa/kZGRKisrc+k7KSnJZbRhwIABatOmjdq2bavRo0dr6dKl5mGcc6mqqtJjjz2mpKQkRUZGKiwsTO+//74OHz4sSdq7d6/Ky8vN0FBb6enpev3111VeXi7pzCjPyJEj5ePjY27vjBkzXD7He+65RwUFBResHwBgLe8f+fAPOTMC4al1XwRfX1+tWbNGn3zyiT744AM999xz+uMf/6jc3FyFhJzpY9GiRerZs+dZy/1YaKjrKEtAQIBuv/12ZWdna+TIkcrOztaIESPk53dmt544cUJdu3bV0qVLz6qpZrThXP2Gh4dr+/btWrdunT744ANNnTpVjz76qLZu3SqHw3FWX0899ZSeffZZPfPMM0pKSlJoaKgmTpyoiooKSVJwcPBFfU4/59Zbb5VhGHrnnXfUvXt3bdy4UXPnzjXnnzhxQtOnT3cZHaoRFBRUJzUAAC6d94cPm+2iDn14ms1mU+/evdW7d29NnTpVbdq00cqVK5WZmanY2FgdOHBA6enpbvebnp6uAQMGaPfu3Vq7dq0ef/xxc16XLl20fPlyRUVFyW63u9Wvn5+fUlJSlJKSomnTpsnhcGjt2rXn/GL/+OOPNXToUP3qV7+SdObk0i+//FJXX321JKl9+/YKDg5WTk6O7r777rOWrxl1qaqqumBNQUFBSktL09KlS7Vv3z516NBBXbp0cdne/Px8tWvXzq1tBQBYy/vDhxfIzc1VTk6Obr75ZkVFRSk3N1fffPONEhMTJZ05V+H+++9XRESEBg4cqPLycm3btk3fffedMjMzL9h33759FRMTo/T0dMXHx7uMnqSnp+upp57S0KFDNWPGDLVq1Ur//Oc/tWLFCk2aNEmtWrU6Z5+rVq3SgQMH1LdvXzVr1kzvvvuuqqurzSthfqp9+/Z67bXX9Mknn6hZs2Z6+umnVVRUZIaPoKAgPfTQQ5o0aZICAgLUu3dvffPNN9q9e7fGjRunqKgoBQcHa/Xq1WrVqpWCgoLOe5ltenq6hgwZot27d5thp8bUqVM1ZMgQxcXF6fbbb5ePj4927typXbt2uYQyAIBncc6HBex2uzZs2KDBgwfrqquu0sMPP6w5c+aYV6ncfffdWrx4sZYsWaKkpCTdcMMNysrKMi9BvRCbzaZRo0Zp586dZ42chISEaMOGDYqLi1NaWpoSExM1btw4lZWVXXAkxOFwaMWKFerXr58SExP1wgsv6JVXXtE111xzzvYPP/ywunTpotTUVN14442KiYk5686kjzzyiH7/+99r6tSpSkxM1IgRI3Ts2DFJZ0ZZ5s2bpxdffFGxsbEaOnToeWvr16+fIiMjlZ+fr1/+8pcu81JTU7Vq1Sp98MEH6t69u3r16qW5c+eaJ/kCABoGm9HAbiHpdDoVERGhkpKSs74gy8rKdPDgQcXHx3MMHx7D7yEAr7X/Q+mvw6Soa6T//qROu77Q9/dPMfIBAEBTcRH3prIC4QMAAFiK8AEAACxF+AAAAJYifAAAAEt5ZfhoYBfooInh9w8ALo1XhQ9/f39J4jkd8Kia37+a30cAgHu86g6nvr6+cjgc5s2pQkJCzKe1AvXNMAydOnVKx44dk8PhOOvZOwCAi+NV4UOS+cj1mgACWM3hcJi/hwAA93ld+LDZbGrZsqWioqJUWVnp6XLQxPj7+zPiAQCXyOvCRw1fX1++BAAA8EJedcIpAADwfoQPAACaHM/eMoDwAQAALOVW+Jg1a5a6d++u8PBwRUVFadiwYcrPz3dpU1ZWpoyMDDVv3lxhYWEaPny4ioqK6rRoAADgvdwKH+vXr1dGRoY2b96sNWvWqLKyUjfffLNOnjxptnnggQf09ttv69VXX9X69et19OhRpaWl1XnhAADAO7l1tcvq1atd3mdlZSkqKkp5eXnq27evSkpK9NJLLyk7O1v9+vWTJC1ZskSJiYnavHmzevXqVXeVAwAAr3RJ53yUlJRIkiIjIyVJeXl5qqysVEpKitkmISFBcXFx2rRp0zn7KC8vl9PpdHkBAIDGq9bho7q6WhMnTlTv3r117bXXSpIKCwsVEBAgh8Ph0jY6OlqFhYXn7GfWrFmKiIgwX61bt65tSQAAwAvUOnxkZGRo165dWrZs2SUVMGXKFJWUlJivI0eOXFJ/AACgYavVHU7vu+8+rVq1Shs2bFCrVq3M6TExMaqoqFBxcbHL6EdRUdF5n4URGBiowMDA2pQBAAC8kFsjH4Zh6L777tPKlSu1du1axcfHu8zv2rWr/P39lZOTY07Lz8/X4cOHlZycXDcVAwAAr+bWyEdGRoays7P15ptvKjw83DyPIyIiQsHBwYqIiNC4ceOUmZmpyMhI2e12jR8/XsnJyVzpAgCAx9k8XYAkN8PHwoULJUk33nijy/QlS5Zo7NixkqS5c+fKx8dHw4cPV3l5uVJTU7VgwYI6KRYAAHg/t8KHYfz8veCDgoI0f/58zZ8/v9ZFAQCAxotnuwAAAEsRPgAAgKUIHwAAwFKEDwAAYCnCBwAAsBThAwAAWIrwAQAALEX4AAAAliJ8AAAASxE+AACApQgfAAA0NRfxuJT6RPgAAACWInwAAABLET4AAIClCB8AAMBShA8AAGApwgcAALAU4QMAAFiK8AEAQFNhs3m6AkmEDwAAYDHCBwAAsBThAwAAWIrwAQAALEX4AAAAliJ8AAAASxE+AACApQgfAADAUoQPAABgKcIHAACwFOEDAABYivABAAAsRfgAAACWInwAAABLET4AAGhyDI+unfABAAAsRfgAAACWInwAAABLET4AAIClCB8AAMBShA8AAJoMm6cLkET4AAAAFiN8AAAASxE+AACApQgfAADAUoQPAABgKcIHAACwFOEDAABYivABAAAsRfgAAACWInwAAABLET4AAIClCB8AAMBShA8AAGApwgcAALAU4QMAAFiK8AEAQFNjGB5dvdvhY8OGDbr11lsVGxsrm82mN954w2W+YRiaOnWqWrZsqeDgYKWkpOirr76qq3oBAICXczt8nDx5Up06ddL8+fPPOf/JJ5/UvHnz9MILLyg3N1ehoaFKTU1VWVnZJRcLAAC8n5+7CwwaNEiDBg065zzDMPTMM8/o4Ycf1tChQyVJf/nLXxQdHa033nhDI0eOvLRqAQCA16vTcz4OHjyowsJCpaSkmNMiIiLUs2dPbdq06ZzLlJeXy+l0urwAAEDjVafho7CwUJIUHR3tMj06Otqc91OzZs1SRESE+WrdunVdlgQAAGrYbJ6uQFIDuNplypQpKikpMV9HjhzxdEkAAKAe1Wn4iImJkSQVFRW5TC8qKjLn/VRgYKDsdrvLCwAANF51Gj7i4+MVExOjnJwcc5rT6VRubq6Sk5PrclUAAMBLuX21y4kTJ7Rv3z7z/cGDB7Vjxw5FRkYqLi5OEydO1OOPP6727dsrPj5ejzzyiGJjYzVs2LC6rBsAAHgpt8PHtm3bdNNNN5nvMzMzJUljxoxRVlaWJk2apJMnT+q3v/2tiouL1adPH61evVpBQUF1VzUAAPBaNsPw8D1Wf8LpdCoiIkIlJSWc/wEAQF069JGUdYvUooN035Y67dqd72+PX+0CAACaFsIHAACwFOEDAABYivABAAAsRfgAAACWInwAAABLET4AAIClCB8AAMBShA8AAGApwgcAALAU4QMAAFiK8AEAQJPj2ce6ET4AAIClCB8AAMBShA8AAJoMm6cLkET4AAAAFiN8AAAASxE+AACApQgfAADAUoQPAABgKcIHAACwFOEDAABYivABAAAsRfgAAACWInwAAABLET4AAIClCB8AAMBShA8AAGApwgcAALAU4QMAAFiK8AEAACxF+AAAAJYifAAAAEsRPgAAgKX8PF0AAACwSOhlUtKdUni0R8sgfAAA0FRcdpU0fJGnq+CwCwAAsBbhAwAAWIrwAQAALEX4AAAAliJ8AAAASxE+AACApQgfAADAUoQPAABgKcIHAACwFOEDAABYivABAAAsRfgAAACWInwAAABLET4AAIClCB8AAMBShA8AAGApwgcaha+KSvV9RZXbyxmGob0FTlVWVddq2fzCUpVVur9eWO9YaZkKSr73dBmNzjel5XyucBvhA15vw5ffaMDcDfqv5z9ye9msTw5p0LMbdf8rn7q97Hu7CpX6zAb9ctFmt5eFtaqrDfWYmaPkWWt1quK0p8tpNAzDUPeZ/1DyrLU6Uc7niotH+IDXW/np15Kkr46dcHvZP204IOlMkHDXK1sOS5K2Hy52e1lYq+JHI1tFznIPVtK4GMYPfy5k9ANuIHwAaPROVxs/3whuqzb4XFE79RY+5s+fryuuuEJBQUHq2bOntmzZUl+rAoALqjz9w8iHwRdmnSHTobbqJXwsX75cmZmZmjZtmrZv365OnTopNTVVx44dq4/VAcAF/fiEYv63Xnf4LFFb9RI+nn76ad1zzz266667dPXVV+uFF15QSEiI/vznP9fH6gDggn58zkfFab4w6wrhA7XlV9cdVlRUKC8vT1OmTDGn+fj4KCUlRZs2bTqrfXl5ucrLfzgBzOl01nVJkqR/nyjX/A/31Uvf8KyaE04lafrbu91atqCkrNbLbvzq37VeFtZyfv/DlRgL1+9Xi7AAD1bTePx4ROnZnH18rl6kRVigMm5q57H113n4+Pe//62qqipFR0e7TI+OjtYXX3xxVvtZs2Zp+vTpdV3GWZzfV2rJx4fqfT3wrEvZx55aFtZ6e+dRT5fQKPG5epe2l4U2rvDhrilTpigzM9N873Q61bp16zpfjyMkQBk3XVnn/cLzDEN6ffu/1CO+ueIig91atryyWm/s+Fr9E6LVIty9/7VVVZ9Zb592LRTrCHJrWVhvz1GnKqqqdV1rh6dLaVS2HvpOZZVVur59C0+XAjc0C/HsKFWdh48WLVrI19dXRUVFLtOLiooUExNzVvvAwEAFBgbWdRlniQwN0IOpCfW+HnjGpIG137cPD7m61stOHsTvFAC4q85POA0ICFDXrl2Vk5NjTquurlZOTo6Sk5PrenUAAMDL1Mthl8zMTI0ZM0bdunVTjx499Mwzz+jkyZO666676mN1AADAi9RL+BgxYoS++eYbTZ06VYWFhbruuuu0evXqs05CBQAATY/NaGC3+3M6nYqIiFBJSYnsdrunywEAABfBne9vnu0CAAAsRfgAAACWInwAAABLET4AAIClCB8AAMBShA8AAGApwgcAALAU4QMAAFiK8AEAACxVL7dXvxQ1N1x1Op0ergQAAFysmu/ti7lxeoMLH6WlpZKk1q1be7gSAADgrtLSUkVERFywTYN7tkt1dbWOHj2q8PBw2Wy2Ou3b6XSqdevWOnLkCM+NaYDYPw0f+6hhY/80bI19/xiGodLSUsXGxsrH58JndTS4kQ8fHx+1atWqXtdht9sb5Y5vLNg/DR/7qGFj/zRsjXn//NyIRw1OOAUAAJYifAAAAEs1qfARGBioadOmKTAw0NOl4BzYPw0f+6hhY/80bOyfHzS4E04BAEDj1qRGPgAAgOcRPgAAgKUIHwAAwFKEDwAAYKkmEz7mz5+vK664QkFBQerZs6e2bNni6ZIapVmzZql79+4KDw9XVFSUhg0bpvz8fJc2ZWVlysjIUPPmzRUWFqbhw4erqKjIpc3hw4d1yy23KCQkRFFRUXrwwQd1+vRplzbr1q1Tly5dFBgYqHbt2ikrK6u+N6/RmT17tmw2myZOnGhOY/941tdff61f/epXat68uYKDg5WUlKRt27aZ8w3D0NSpU9WyZUsFBwcrJSVFX331lUsfx48fV3p6uux2uxwOh8aNG6cTJ064tPnss890/fXXKygoSK1bt9aTTz5pyfZ5u6qqKj3yyCOKj49XcHCwrrzySj322GMuzzNhH10EowlYtmyZERAQYPz5z382du/ebdxzzz2Gw+EwioqKPF1ao5OammosWbLE2LVrl7Fjxw5j8ODBRlxcnHHixAmzzb333mu0bt3ayMnJMbZt22b06tXL+MUvfmHOP336tHHttdcaKSkpxqeffmq8++67RosWLYwpU6aYbQ4cOGCEhIQYmZmZxp49e4znnnvO8PX1NVavXm3p9nqzLVu2GFdccYXRsWNHY8KECeZ09o/nHD9+3GjTpo0xduxYIzc31zhw4IDx/vvvG/v27TPbzJ4924iIiDDeeOMNY+fOncZ//dd/GfHx8cb3339vthk4cKDRqVMnY/PmzcbGjRuNdu3aGaNGjTLnl5SUGNHR0UZ6erqxa9cu45VXXjGCg4ONF1980dLt9UYzZ840mjdvbqxatco4ePCg8eqrrxphYWHGs88+a7ZhH/28JhE+evToYWRkZJjvq6qqjNjYWGPWrFkerKppOHbsmCHJWL9+vWEYhlFcXGz4+/sbr776qtlm7969hiRj06ZNhmEYxrvvvmv4+PgYhYWFZpuFCxcadrvdKC8vNwzDMCZNmmRcc801LusaMWKEkZqaWt+b1CiUlpYa7du3N9asWWPccMMNZvhg/3jWQw89ZPTp0+e886urq42YmBjjqaeeMqcVFxcbgYGBxiuvvGIYhmHs2bPHkGRs3brVbPPee+8ZNpvN+Prrrw3DMIwFCxYYzZo1M/dXzbo7dOhQ15vU6Nxyyy3Gb37zG5dpaWlpRnp6umEY7KOL1egPu1RUVCgvL08pKSnmNB8fH6WkpGjTpk0erKxpKCkpkSRFRkZKkvLy8lRZWemyPxISEhQXF2fuj02bNikpKUnR0dFmm9TUVDmdTu3evdts8+M+atqwTy9ORkaGbrnllrM+Q/aPZ7311lvq1q2b7rjjDkVFRalz585atGiROf/gwYMqLCx0+WwjIiLUs2dPl/3jcDjUrVs3s01KSop8fHyUm5trtunbt68CAgLMNqmpqcrPz9d3331X35vp1X7xi18oJydHX375pSRp586d+uijjzRo0CBJ7KOL1eAeLFfX/v3vf6uqqsrlH0pJio6O1hdffOGhqpqG6upqTZw4Ub1799a1114rSSosLFRAQIAcDodL2+joaBUWFpptzrW/auZdqI3T6dT333+v4ODg+tikRmHZsmXavn27tm7detY89o9nHThwQAsXLlRmZqb+53/+R1u3btX999+vgIAAjRkzxvx8z/XZ/vizj4qKcpnv5+enyMhIlzbx8fFn9VEzr1mzZvWyfY3B5MmT5XQ6lZCQIF9fX1VVVWnmzJlKT0+XJPbRRWr04QOek5GRoV27dumjjz7ydCn4jyNHjmjChAlas2aNgoKCPF0OfqK6ulrdunXTE088IUnq3Lmzdu3apRdeeEFjxozxcHWQpL///e9aunSpsrOzdc0112jHjh2aOHGiYmNj2UduaPSHXVq0aCFfX9+zztYvKipSTEyMh6pq/O677z6tWrVKH374oVq1amVOj4mJUUVFhYqLi13a/3h/xMTEnHN/1cy7UBu73c7/qi8gLy9Px44dU5cuXeTn5yc/Pz+tX79e8+bNk5+fn6Kjo9k/HtSyZUtdffXVLtMSExN1+PBhST98vhf69ywmJkbHjh1zmX/69GkdP37crX2Ic3vwwQc1efJkjRw5UklJSRo9erQeeOABzZo1SxL76GI1+vAREBCgrl27Kicnx5xWXV2tnJwcJScne7CyxskwDN13331auXKl1q5de9awYdeuXeXv7++yP/Lz83X48GFzfyQnJ+vzzz93+cu5Zs0a2e128x/m5ORklz5q2rBPL6x///76/PPPtWPHDvPVrVs3paenm39m/3hO7969z7o0/csvv1SbNm0kSfHx8YqJiXH5bJ1Op3Jzc132T3FxsfLy8sw2a9euVXV1tXr27Gm22bBhgyorK802a9asUYcOHbx+OL++nTp1Sj4+rl+dvr6+qq6ulsQ+umiePuPVCsuWLTMCAwONrKwsY8+ePcZvf/tbw+FwuJytj7rxu9/9zoiIiDDWrVtnFBQUmK9Tp06Zbe69914jLi7OWLt2rbFt2zYjOTnZSE5ONufXXMp58803Gzt27DBWr15tXHbZZee8lPPBBx809u7da8yfP59LOWvpx1e7GAb7x5O2bNli+Pn5GTNnzjS++uorY+nSpUZISIjxt7/9zWwze/Zsw+FwGG+++abx2WefGUOHDj3nZZydO3c2cnNzjY8++sho3769y2WcxcXFRnR0tDF69Ghj165dxrJly4yQkJBGcxlnfRozZoxx+eWXm5farlixwmjRooUxadIksw376Oc1ifBhGIbx3HPPGXFxcUZAQIDRo0cPY/PmzZ4uqVGSdM7XkiVLzDbff/+98d///d9Gs2bNjJCQEOO2224zCgoKXPo5dOiQMWjQICM4ONho0aKF8fvf/96orKx0afPhhx8a1113nREQEGC0bdvWZR24eD8NH+wfz3r77beNa6+91ggMDDQSEhKMP/3pTy7zq6urjUceecSIjo42AgMDjf79+xv5+fkubb799ltj1KhRRlhYmGG324277rrLKC0tdWmzc+dOo0+fPkZgYKBx+eWXG7Nnz673bWsMnE6nMWHCBCMuLs4ICgoy2rZta/zxj390uSSWffTzbIbxo9uyAQAA1LNGf84HAABoWAgfAADAUoQPAABgKcIHAACwFOEDAABYivABAAAsRfgAAACWInwAAABLET4AAIClCB8AAMBShA8AAGApwgcAALDU/wfbnjItaCtKlQAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "plt.plot(df_service_multi.servers_pending, label=\"servers pending\")\n",
-    "plt.plot(df_service_multi.servers_active, label=\"servers active\")\n",
-    "\n",
-    "plt.legend()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 156,
-   "id": "dc4e17cd",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>timestamp</th>\n",
-       "      <th>server_id</th>\n",
-       "      <th>host_id</th>\n",
-       "      <th>mem_capacity</th>\n",
-       "      <th>cpu_count</th>\n",
-       "      <th>cpu_limit</th>\n",
-       "      <th>cpu_time_active</th>\n",
-       "      <th>cpu_time_idle</th>\n",
-       "      <th>cpu_time_steal</th>\n",
-       "      <th>cpu_time_lost</th>\n",
-       "      <th>uptime</th>\n",
-       "      <th>downtime</th>\n",
-       "      <th>provision_time</th>\n",
-       "      <th>boot_time</th>\n",
-       "      <th>absolute_timestamp</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1970-01-01 00:05:00+00:00</td>\n",
-       "      <td>b'\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x06\\xc4]\\x1...</td>\n",
-       "      <td>b'\\xe2 \\xa89{\\x1d\\xcd\\xaf\\x00\\x00\\x00\\x00\\x00\\...</td>\n",
-       "      <td>181</td>\n",
-       "      <td>1</td>\n",
-       "      <td>25600.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2624</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>300000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1970-01-01 00:00:00+00:00</td>\n",
-       "      <td>1970-01-01 00:00:00+00:00</td>\n",
-       "      <td>2013-08-12 13:35:46+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1970-01-01 00:05:00+00:00</td>\n",
-       "      <td>b'\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x1b9\\x89jQ\\...</td>\n",
-       "      <td>b'\\xe2 \\xa89{\\x1d\\xcd\\xaf\\x00\\x00\\x00\\x00\\x00\\...</td>\n",
-       "      <td>260</td>\n",
-       "      <td>1</td>\n",
-       "      <td>25600.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2624</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>300000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1970-01-01 00:00:00+00:00</td>\n",
-       "      <td>1970-01-01 00:00:00+00:00</td>\n",
-       "      <td>2013-08-12 13:35:46+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1970-01-01 00:05:00+00:00</td>\n",
-       "      <td>b'\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00,\\x82\\x9a\\xb...</td>\n",
-       "      <td>b'\\xe2 \\xa89{\\x1d\\xcd\\xaf\\x00\\x00\\x00\\x00\\x00\\...</td>\n",
-       "      <td>250</td>\n",
-       "      <td>1</td>\n",
-       "      <td>25600.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2622</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>300000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1970-01-01 00:00:00+00:00</td>\n",
-       "      <td>1970-01-01 00:00:00+00:00</td>\n",
-       "      <td>2013-08-12 13:35:46+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1970-01-01 00:05:00+00:00</td>\n",
-       "      <td>b'\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00&gt;\\xe5x\\x90A\\...</td>\n",
-       "      <td>b'\\xe2 \\xa89{\\x1d\\xcd\\xaf\\x00\\x00\\x00\\x00\\x00\\...</td>\n",
-       "      <td>125</td>\n",
-       "      <td>1</td>\n",
-       "      <td>25600.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2624</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>300000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1970-01-01 00:00:00+00:00</td>\n",
-       "      <td>1970-01-01 00:00:00+00:00</td>\n",
-       "      <td>2013-08-12 13:35:46+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1970-01-01 00:05:00+00:00</td>\n",
-       "      <td>b'\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00e~\\xec\\xdd&lt;\\...</td>\n",
-       "      <td>b'\\xe2 \\xa89{\\x1d\\xcd\\xaf\\x00\\x00\\x00\\x00\\x00\\...</td>\n",
-       "      <td>157</td>\n",
-       "      <td>1</td>\n",
-       "      <td>25600.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2951</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>300000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1970-01-01 00:00:00+00:00</td>\n",
-       "      <td>1970-01-01 00:00:00+00:00</td>\n",
-       "      <td>2013-08-12 13:35:46+00:00</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                  timestamp  \\\n",
-       "0 1970-01-01 00:05:00+00:00   \n",
-       "1 1970-01-01 00:05:00+00:00   \n",
-       "2 1970-01-01 00:05:00+00:00   \n",
-       "3 1970-01-01 00:05:00+00:00   \n",
-       "4 1970-01-01 00:05:00+00:00   \n",
-       "\n",
-       "                                           server_id  \\\n",
-       "0  b'\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x06\\xc4]\\x1...   \n",
-       "1  b'\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x1b9\\x89jQ\\...   \n",
-       "2  b'\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00,\\x82\\x9a\\xb...   \n",
-       "3  b'\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00>\\xe5x\\x90A\\...   \n",
-       "4  b'\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00e~\\xec\\xdd<\\...   \n",
-       "\n",
-       "                                             host_id  mem_capacity  cpu_count  \\\n",
-       "0  b'\\xe2 \\xa89{\\x1d\\xcd\\xaf\\x00\\x00\\x00\\x00\\x00\\...           181          1   \n",
-       "1  b'\\xe2 \\xa89{\\x1d\\xcd\\xaf\\x00\\x00\\x00\\x00\\x00\\...           260          1   \n",
-       "2  b'\\xe2 \\xa89{\\x1d\\xcd\\xaf\\x00\\x00\\x00\\x00\\x00\\...           250          1   \n",
-       "3  b'\\xe2 \\xa89{\\x1d\\xcd\\xaf\\x00\\x00\\x00\\x00\\x00\\...           125          1   \n",
-       "4  b'\\xe2 \\xa89{\\x1d\\xcd\\xaf\\x00\\x00\\x00\\x00\\x00\\...           157          1   \n",
-       "\n",
-       "   cpu_limit  cpu_time_active  cpu_time_idle  cpu_time_steal  cpu_time_lost  \\\n",
-       "0    25600.0                0           2624               0              0   \n",
-       "1    25600.0                0           2624               0              0   \n",
-       "2    25600.0                2           2622               0              0   \n",
-       "3    25600.0                0           2624               0              0   \n",
-       "4    25600.0                2           2951               0              0   \n",
-       "\n",
-       "   uptime  downtime            provision_time                 boot_time  \\\n",
-       "0  300000         0 1970-01-01 00:00:00+00:00 1970-01-01 00:00:00+00:00   \n",
-       "1  300000         0 1970-01-01 00:00:00+00:00 1970-01-01 00:00:00+00:00   \n",
-       "2  300000         0 1970-01-01 00:00:00+00:00 1970-01-01 00:00:00+00:00   \n",
-       "3  300000         0 1970-01-01 00:00:00+00:00 1970-01-01 00:00:00+00:00   \n",
-       "4  300000         0 1970-01-01 00:00:00+00:00 1970-01-01 00:00:00+00:00   \n",
-       "\n",
-       "         absolute_timestamp  \n",
-       "0 2013-08-12 13:35:46+00:00  \n",
-       "1 2013-08-12 13:35:46+00:00  \n",
-       "2 2013-08-12 13:35:46+00:00  \n",
-       "3 2013-08-12 13:35:46+00:00  \n",
-       "4 2013-08-12 13:35:46+00:00  "
-      ]
-     },
-     "execution_count": 156,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_server_single.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 157,
-   "id": "b0e6c7bf",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "utilization = df_host_single.cpu_utilization.to_numpy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 161,
-   "id": "aea7b79d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f6f842c6470>]"
-      ]
-     },
-     "execution_count": 161,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "window = 2000\n",
-    "avg_utilization = []\n",
-    "\n",
-    "for ind in range(len(utilization) - window + 1):\n",
-    "    avg_utilization.append(np.mean(utilization[ind:ind+window]))\n",
-    "    \n",
-    "plt.plot(avg_utilization)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 129,
-   "id": "575f824b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f6f872c7fa0>]"
-      ]
-     },
-     "execution_count": 129,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sum_util = []\n",
-    "\n",
-    "last_util = 0\n",
-    "for util in utilization:\n",
-    "    sum_util.append(util + last_util)\n",
-    "    last_util = sum_util[-1]\n",
-    "    \n",
-    "plt.plot(sum_util)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d99dfce2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output_file = \"../Python_scripts/meta_small.parquet\"\n",
-    "output_file_path = Path(output_file)\n",
-    "\n",
-    "df_meta_new.to_parquet(output_file_path, index=False)\n",
-    "\n",
-    "output_file = \"../Python_scripts/trace_small.parquet\"\n",
-    "output_file_path = Path(output_file)\n",
-    "df_trace_new.to_parquet(output_file_path, index=False)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/opendc-experiments/opendc-experiments-scenario/src/main/Python_scripts/multimodel-retry.ipynb b/opendc-experiments/opendc-experiments-scenario/src/main/Python_scripts/multimodel-retry.ipynb
deleted file mode 100644
index 02d91831b..000000000
--- a/opendc-experiments/opendc-experiments-scenario/src/main/Python_scripts/multimodel-retry.ipynb
+++ /dev/null
@@ -1,40 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "initial_id",
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import pandas as pd\n",
-    "import os"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/opendc-experiments/opendc-experiments-scenario/src/main/Python_scripts/multimodel.ipynb b/opendc-experiments/opendc-experiments-scenario/src/main/Python_scripts/multimodel.ipynb
deleted file mode 100644
index 13ac2ee3e..000000000
--- a/opendc-experiments/opendc-experiments-scenario/src/main/Python_scripts/multimodel.ipynb
+++ /dev/null
@@ -1,406 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# OpenDC MultiModel Demo"
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "fb60c8069ab28634"
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "> this file assembles multiple simulation models into a single simulation tool -- <b>the multimodel</b>\n",
-    "> we leverage the outputs of the simulation models, in the same plot\n",
-    "> and more :>"
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "6db427bbc013a04f"
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### 0. Imports"
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "613ddc7764c074d2"
-  },
-  {
-   "cell_type": "code",
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import pandas as pd\n",
-    "import os\n",
-    "import pyarrow.parquet as pq\n",
-    "import fastparquet as fp"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-04-25T15:25:44.461124Z",
-     "start_time": "2024-04-25T15:25:44.458619Z"
-    }
-   },
-   "id": "bfbf3ccebf48cde4",
-   "execution_count": 45
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### 1. Load the outputs of the simulation models"
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "46656be1992088cc"
-  },
-  {
-   "cell_type": "code",
-   "outputs": [
-    {
-     "ename": "FileNotFoundError",
-     "evalue": "[Errno 2] No such file or directory: '../../../../../demo/output/'",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
-      "\u001B[0;31mFileNotFoundError\u001B[0m                         Traceback (most recent call last)",
-      "Cell \u001B[0;32mIn[46], line 1\u001B[0m\n\u001B[0;32m----> 1\u001B[0m \u001B[43mos\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mchdir\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43m../../../../../demo/output/\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[1;32m      2\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mWe are now in: \u001B[39m\u001B[38;5;124m\"\u001B[39m, os\u001B[38;5;241m.\u001B[39mgetcwd())\n\u001B[1;32m      4\u001B[0m \u001B[38;5;66;03m# loop through the files, take only the first word of the file name, which is in format modelname-*\u001B[39;00m\n",
-      "\u001B[0;31mFileNotFoundError\u001B[0m: [Errno 2] No such file or directory: '../../../../../demo/output/'"
-     ]
-    }
-   ],
-   "source": [
-    "os.chdir('../../../../../demo/output/')\n",
-    "print(\"We are now in: \", os.getcwd())\n",
-    "\n",
-    "# loop through the files, take only the first word of the file name, which is in format modelname-*\n",
-    "model_names = [file.split('-')[0] for file in os.listdir()]\n",
-    "directories = [d for d in os.listdir() if os.path.isdir(d)]\n",
-    "\n",
-    "# output data is a list of lists -- there are multiple arrays, and each array has data for host, server, service, in this order\n",
-    "# basically we will have\n",
-    "host_data = []\n",
-    "server_data = []\n",
-    "service_data = []\n",
-    "\n",
-    "print(directories)\n",
-    "\n",
-    "# loop through each directory and add the data to the list\n",
-    "for directory in directories:\n",
-    "    host_data.append(pd.read_parquet(os.path.join(directory, 'seed=0/host.parquet')))\n",
-    "    server_data.append(pd.read_parquet(os.path.join(directory, 'seed=0/server.parquet')))\n",
-    "    service_data.append(pd.read_parquet(os.path.join(directory, 'seed=0/service.parquet')))"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-04-25T15:25:44.486010Z",
-     "start_time": "2024-04-25T15:25:44.466660Z"
-    }
-   },
-   "id": "df9bb28b2ca1ced1",
-   "execution_count": 46
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### 2. Define useful functions"
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "434a191b3b902e14"
-  },
-  {
-   "cell_type": "code",
-   "outputs": [],
-   "source": [
-    "def mean_of_chunks(series, chunk_size):\n",
-    "    return series.groupby(np.arange(len(series)) // chunk_size).mean(numeric_only=True)"
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "5d832e6bbd09c8ac",
-   "execution_count": null
-  },
-  {
-   "cell_type": "code",
-   "outputs": [],
-   "source": [
-    "def plot_singular_model(single_model):\n",
-    "    plt.figure(figsize=(20,10))\n",
-    "    plt.plot(single_model)\n",
-    "    plt.show()"
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "6b1e2a5c7c656307",
-   "execution_count": null
-  },
-  {
-   "cell_type": "code",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "Index(['timestamp', 'absolute_timestamp', 'host_id', 'cpu_count',\n       'mem_capacity', 'guests_terminated', 'guests_running', 'guests_error',\n       'guests_invalid', 'cpu_limit', 'cpu_usage', 'cpu_demand',\n       'cpu_utilization', 'cpu_time_active', 'cpu_time_idle', 'cpu_time_steal',\n       'cpu_time_lost', 'power_draw', 'energy_usage', 'carbon_intensity',\n       'carbon_emission', 'uptime', 'downtime', 'boot_time'],\n      dtype='object')"
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def plot_multi_model_carbon(multi_model):\n",
-    "    plt.figure(figsize=(20,10))\n",
-    "    for model in multi_model:\n",
-    "        plt.ylabel(\"POwer draw [W]\")\n",
-    "        plt.xlabel(\"Seconds [S]\")\n",
-    "        \n",
-    "        # make the y-label between 0-500\n",
-    "        plt.ylim(0, 5)\n",
-    "        \n",
-    "        # add some shadows up and down as a standard deviation\n",
-    "        plt.fill_between(model.index, model - 0.5, model + 0.5, color='gray', alpha=0.5)\n",
-    "        \n",
-    "        # plt.ylim(0, 500)\n",
-    "        plt.plot(model)\n",
-    "    plt.show()\n",
-    "    \n",
-    "host_data[0].columns"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-04-25T15:25:44.498972Z",
-     "start_time": "2024-04-25T15:25:44.494724Z"
-    }
-   },
-   "id": "295c7f5fd087324d",
-   "execution_count": 47
-  },
-  {
-   "cell_type": "code",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "Index(['timestamp', 'absolute_timestamp', 'host_id', 'cpu_count',\n       'mem_capacity', 'guests_terminated', 'guests_running', 'guests_error',\n       'guests_invalid', 'cpu_limit', 'cpu_usage', 'cpu_demand',\n       'cpu_utilization', 'cpu_time_active', 'cpu_time_idle', 'cpu_time_steal',\n       'cpu_time_lost', 'power_draw', 'energy_usage', 'carbon_intensity',\n       'carbon_emission', 'uptime', 'downtime', 'boot_time'],\n      dtype='object')"
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def plot_multi_model_power(multi_model):\n",
-    "    plt.figure(figsize=(20,10))\n",
-    "    for model in multi_model:\n",
-    "        plt.ylabel(\"POwer draw [W]\")\n",
-    "        plt.xlabel(\"Seconds [S]\")\n",
-    "\n",
-    "        # make the y-label between 0-500\n",
-    "        plt.ylim(0, 500)\n",
-    "\n",
-    "        # add some shadows up and down as a standard deviation\n",
-    "        plt.fill_between(model.index, model - 50, model + 50, color='gray', alpha=0.5)\n",
-    "\n",
-    "        plt.ylim(0, 500)\n",
-    "        plt.plot(model)\n",
-    "    plt.show()\n",
-    "\n",
-    "host_data[0].columns"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-04-25T15:25:44.504589Z",
-     "start_time": "2024-04-25T15:25:44.501270Z"
-    }
-   },
-   "id": "8c44c2b2ca7f0c24",
-   "execution_count": 48
-  },
-  {
-   "cell_type": "code",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "<Figure size 2000x1000 with 1 Axes>",
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "False\n"
-     ]
-    }
-   ],
-   "source": [
-    "### 3. Plot the outputs of the simulation models\n",
-    "simulation_data = [\n",
-    "    mean_of_chunks(host_data[0]['power_draw'], 1000),\n",
-    "    mean_of_chunks(host_data[1]['power_draw'], 1000),\n",
-    "    mean_of_chunks(host_data[2]['power_draw'], 1000),\n",
-    "    mean_of_chunks(host_data[3]['power_draw'], 1000),\n",
-    "]\n",
-    "\n",
-    "plot_multi_model_power(simulation_data)\n",
-    "areEqual = host_data[0].equals(host_data[3])\n",
-    "print(areEqual)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-04-25T15:25:44.668677Z",
-     "start_time": "2024-04-25T15:25:44.506909Z"
-    }
-   },
-   "id": "d46afc205586d40a",
-   "execution_count": 49
-  },
-  {
-   "cell_type": "code",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "<Figure size 2000x1000 with 1 Axes>",
-      "image/png": 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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "False\n"
-     ]
-    }
-   ],
-   "source": [
-    "### 3. Plot the outputs of the simulation models\n",
-    "simulation_data = [\n",
-    "    mean_of_chunks(host_data[0]['carbon_emission'], 1000),\n",
-    "    mean_of_chunks(host_data[1]['carbon_emission'], 1000),\n",
-    "    mean_of_chunks(host_data[2]['carbon_emission'], 1000),\n",
-    "    mean_of_chunks(host_data[3]['carbon_emission'], 1000),\n",
-    "]\n",
-    "\n",
-    "plot_multi_model_carbon(simulation_data)\n",
-    "areEqual = host_data[0].equals(host_data[3])\n",
-    "print(areEqual)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-04-25T15:25:44.844525Z",
-     "start_time": "2024-04-25T15:25:44.670271Z"
-    }
-   },
-   "id": "4f5035d17712d020",
-   "execution_count": 50
-  },
-  {
-   "cell_type": "code",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "         timestamp  absolute_timestamp                               host_id  \\\n0           300000       1376314846000  e220a839-7b1d-cdaf-0000-000000000003   \n1           600000       1376315146000  e220a839-7b1d-cdaf-0000-000000000003   \n2           900000       1376315446000  e220a839-7b1d-cdaf-0000-000000000003   \n3          1200000       1376315746000  e220a839-7b1d-cdaf-0000-000000000003   \n4          1500000       1376316046000  e220a839-7b1d-cdaf-0000-000000000003   \n...            ...                 ...                                   ...   \n43200  12960300000       1389274846000  e220a839-7b1d-cdaf-0000-000000000003   \n43201  12960600000       1389275146000  e220a839-7b1d-cdaf-0000-000000000003   \n43202  12960900000       1389275446000  e220a839-7b1d-cdaf-0000-000000000003   \n43203  12961200000       1389275746000  e220a839-7b1d-cdaf-0000-000000000003   \n43204  12961452000       1389275998000  e220a839-7b1d-cdaf-0000-000000000003   \n\n       cpu_count  mem_capacity  guests_terminated  guests_running  \\\n0             32         82000                  0              13   \n1             32         82000                  0              13   \n2             32         82000                  0              13   \n3             32         82000                  0              13   \n4             32         82000                  0              13   \n...          ...           ...                ...             ...   \n43200         32         82000                  0               1   \n43201         32         82000                  0               1   \n43202         32         82000                  0               1   \n43203         32         82000                  0               1   \n43204         32         82000                  0               0   \n\n       guests_error  guests_invalid  cpu_limit  ...  cpu_time_idle  \\\n0                 0               0    80000.0  ...        5854411   \n1                 0               0    80000.0  ...        6212060   \n2                 0               0    80000.0  ...        6320948   \n3                 0               0    80000.0  ...        7751754   \n4                 0               0    80000.0  ...        7634338   \n...             ...             ...        ...  ...            ...   \n43200             0               0    80000.0  ...        8332490   \n43201             0               0    80000.0  ...        8279284   \n43202             0               0    80000.0  ...        8168362   \n43203             0               0    80000.0  ...        8361859   \n43204             0               0    80000.0  ...        6879409   \n\n       cpu_time_steal  cpu_time_lost  power_draw  energy_usage  \\\n0                 301              0  208.909042  62672.853742   \n1                   0              0  206.593026  61977.907905   \n2                   0              0  205.977536  61793.260817   \n3                   0              0  201.070427  60321.128023   \n4                   0              0  201.287668  60386.300489   \n...               ...            ...         ...           ...   \n43200               0              0  200.365120  60103.594812   \n43201               0              0  200.543777  60118.111546   \n43202               0              0  200.296912  60151.283474   \n43203               0              0  200.475491  60097.645417   \n43204               0              0  200.000000  50519.823643   \n\n       carbon_intensity  carbon_emission  uptime  downtime  boot_time  \n0             43.004721         0.748675  300000         0          0  \n1             42.607796         0.733539  300000         0          0  \n2             42.607796         0.731354  300000         0          0  \n3             42.607796         0.713931  300000         0          0  \n4             42.292972         0.709421  300000         0          0  \n...                 ...              ...     ...       ...        ...  \n43200         28.089877         0.468973  300000         0          0  \n43201         28.089877         0.469086  300000         0          0  \n43202         28.089877         0.469345  300000         0          0  \n43203         28.089877         0.468927  300000         0          0  \n43204         28.089877         0.394193  252000         0          0  \n\n[43205 rows x 24 columns]",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>timestamp</th>\n      <th>absolute_timestamp</th>\n      <th>host_id</th>\n      <th>cpu_count</th>\n      <th>mem_capacity</th>\n      <th>guests_terminated</th>\n      <th>guests_running</th>\n      <th>guests_error</th>\n      <th>guests_invalid</th>\n      <th>cpu_limit</th>\n      <th>...</th>\n      <th>cpu_time_idle</th>\n      <th>cpu_time_steal</th>\n      <th>cpu_time_lost</th>\n      <th>power_draw</th>\n      <th>energy_usage</th>\n      <th>carbon_intensity</th>\n      <th>carbon_emission</th>\n      <th>uptime</th>\n      <th>downtime</th>\n      <th>boot_time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>300000</td>\n      <td>1376314846000</td>\n      <td>e220a839-7b1d-cdaf-0000-000000000003</td>\n      <td>32</td>\n      <td>82000</td>\n      <td>0</td>\n      <td>13</td>\n      <td>0</td>\n      <td>0</td>\n      <td>80000.0</td>\n      <td>...</td>\n      <td>5854411</td>\n      <td>301</td>\n      <td>0</td>\n      <td>208.909042</td>\n      <td>62672.853742</td>\n      <td>43.004721</td>\n      <td>0.748675</td>\n      <td>300000</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>600000</td>\n      <td>1376315146000</td>\n      <td>e220a839-7b1d-cdaf-0000-000000000003</td>\n      <td>32</td>\n      <td>82000</td>\n      <td>0</td>\n      <td>13</td>\n      <td>0</td>\n      <td>0</td>\n      <td>80000.0</td>\n      <td>...</td>\n      <td>6212060</td>\n      <td>0</td>\n      <td>0</td>\n      <td>206.593026</td>\n      <td>61977.907905</td>\n      <td>42.607796</td>\n      <td>0.733539</td>\n      <td>300000</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>900000</td>\n      <td>1376315446000</td>\n      <td>e220a839-7b1d-cdaf-0000-000000000003</td>\n      <td>32</td>\n      <td>82000</td>\n      <td>0</td>\n      <td>13</td>\n      <td>0</td>\n      <td>0</td>\n      <td>80000.0</td>\n      <td>...</td>\n      <td>6320948</td>\n      <td>0</td>\n      <td>0</td>\n      <td>205.977536</td>\n      <td>61793.260817</td>\n      <td>42.607796</td>\n      <td>0.731354</td>\n      <td>300000</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1200000</td>\n      <td>1376315746000</td>\n      <td>e220a839-7b1d-cdaf-0000-000000000003</td>\n      <td>32</td>\n      <td>82000</td>\n      <td>0</td>\n      <td>13</td>\n      <td>0</td>\n      <td>0</td>\n      <td>80000.0</td>\n      <td>...</td>\n      <td>7751754</td>\n      <td>0</td>\n      <td>0</td>\n      <td>201.070427</td>\n      <td>60321.128023</td>\n      <td>42.607796</td>\n      <td>0.713931</td>\n      <td>300000</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1500000</td>\n      <td>1376316046000</td>\n      <td>e220a839-7b1d-cdaf-0000-000000000003</td>\n      <td>32</td>\n      <td>82000</td>\n      <td>0</td>\n      <td>13</td>\n      <td>0</td>\n      <td>0</td>\n      <td>80000.0</td>\n      <td>...</td>\n      <td>7634338</td>\n      <td>0</td>\n      <td>0</td>\n      <td>201.287668</td>\n      <td>60386.300489</td>\n      <td>42.292972</td>\n      <td>0.709421</td>\n      <td>300000</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>43200</th>\n      <td>12960300000</td>\n      <td>1389274846000</td>\n      <td>e220a839-7b1d-cdaf-0000-000000000003</td>\n      <td>32</td>\n      <td>82000</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>80000.0</td>\n      <td>...</td>\n      <td>8332490</td>\n      <td>0</td>\n      <td>0</td>\n      <td>200.365120</td>\n      <td>60103.594812</td>\n      <td>28.089877</td>\n      <td>0.468973</td>\n      <td>300000</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>43201</th>\n      <td>12960600000</td>\n      <td>1389275146000</td>\n      <td>e220a839-7b1d-cdaf-0000-000000000003</td>\n      <td>32</td>\n      <td>82000</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>80000.0</td>\n      <td>...</td>\n      <td>8279284</td>\n      <td>0</td>\n      <td>0</td>\n      <td>200.543777</td>\n      <td>60118.111546</td>\n      <td>28.089877</td>\n      <td>0.469086</td>\n      <td>300000</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>43202</th>\n      <td>12960900000</td>\n      <td>1389275446000</td>\n      <td>e220a839-7b1d-cdaf-0000-000000000003</td>\n      <td>32</td>\n      <td>82000</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>80000.0</td>\n      <td>...</td>\n      <td>8168362</td>\n      <td>0</td>\n      <td>0</td>\n      <td>200.296912</td>\n      <td>60151.283474</td>\n      <td>28.089877</td>\n      <td>0.469345</td>\n      <td>300000</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>43203</th>\n      <td>12961200000</td>\n      <td>1389275746000</td>\n      <td>e220a839-7b1d-cdaf-0000-000000000003</td>\n      <td>32</td>\n      <td>82000</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>80000.0</td>\n      <td>...</td>\n      <td>8361859</td>\n      <td>0</td>\n      <td>0</td>\n      <td>200.475491</td>\n      <td>60097.645417</td>\n      <td>28.089877</td>\n      <td>0.468927</td>\n      <td>300000</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>43204</th>\n      <td>12961452000</td>\n      <td>1389275998000</td>\n      <td>e220a839-7b1d-cdaf-0000-000000000003</td>\n      <td>32</td>\n      <td>82000</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>80000.0</td>\n      <td>...</td>\n      <td>6879409</td>\n      <td>0</td>\n      <td>0</td>\n      <td>200.000000</td>\n      <td>50519.823643</td>\n      <td>28.089877</td>\n      <td>0.394193</td>\n      <td>252000</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>43205 rows × 24 columns</p>\n</div>"
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "host_data[0]"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-04-25T15:25:44.862985Z",
-     "start_time": "2024-04-25T15:25:44.846638Z"
-    }
-   },
-   "id": "258b4dc49154822a",
-   "execution_count": 51
-  },
-  {
-   "cell_type": "code",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "         timestamp  absolute_timestamp  hosts_up  hosts_down  servers_pending  \\\n0           300000       1376314846000         1           0               31   \n1           600000       1376315146000         1           0               31   \n2           900000       1376315446000         1           0               31   \n3          1200000       1376315746000         1           0               31   \n4          1500000       1376316046000         1           0               31   \n...            ...                 ...       ...         ...              ...   \n43200  12960300000       1389274846000         1           0                0   \n43201  12960600000       1389275146000         1           0                0   \n43202  12960900000       1389275446000         1           0                0   \n43203  12961200000       1389275746000         1           0                0   \n43204  12961452000       1389275998000         1           0                0   \n\n       servers_active  attempts_success  attempts_failure  attempts_error  \n0                  13                13                 0               0  \n1                  13                13                 0               0  \n2                  13                13                 0               0  \n3                  13                13                 0               0  \n4                  13                13                 0               0  \n...               ...               ...               ...             ...  \n43200               1                49                 1               0  \n43201               1                49                 1               0  \n43202               1                49                 1               0  \n43203               1                49                 1               0  \n43204               0                49                 1               0  \n\n[43205 rows x 9 columns]",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>timestamp</th>\n      <th>absolute_timestamp</th>\n      <th>hosts_up</th>\n      <th>hosts_down</th>\n      <th>servers_pending</th>\n      <th>servers_active</th>\n      <th>attempts_success</th>\n      <th>attempts_failure</th>\n      <th>attempts_error</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>300000</td>\n      <td>1376314846000</td>\n      <td>1</td>\n      <td>0</td>\n      <td>31</td>\n      <td>13</td>\n      <td>13</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>600000</td>\n      <td>1376315146000</td>\n      <td>1</td>\n      <td>0</td>\n      <td>31</td>\n      <td>13</td>\n      <td>13</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>900000</td>\n      <td>1376315446000</td>\n      <td>1</td>\n      <td>0</td>\n      <td>31</td>\n      <td>13</td>\n      <td>13</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1200000</td>\n      <td>1376315746000</td>\n      <td>1</td>\n      <td>0</td>\n      <td>31</td>\n      <td>13</td>\n      <td>13</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1500000</td>\n      <td>1376316046000</td>\n      <td>1</td>\n      <td>0</td>\n      <td>31</td>\n      <td>13</td>\n      <td>13</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>43200</th>\n      <td>12960300000</td>\n      <td>1389274846000</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>49</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>43201</th>\n      <td>12960600000</td>\n      <td>1389275146000</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>49</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>43202</th>\n      <td>12960900000</td>\n      <td>1389275446000</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>49</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>43203</th>\n      <td>12961200000</td>\n      <td>1389275746000</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>49</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>43204</th>\n      <td>12961452000</td>\n      <td>1389275998000</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>49</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>43205 rows × 9 columns</p>\n</div>"
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# print host_data as a table\n",
-    "service_data[0]\n"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-04-25T15:25:44.870834Z",
-     "start_time": "2024-04-25T15:25:44.864608Z"
-    }
-   },
-   "id": "7f7dcf086ce45547",
-   "execution_count": 52
-  },
-  {
-   "cell_type": "code",
-   "outputs": [],
-   "source": [],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-04-25T15:25:44.875556Z",
-     "start_time": "2024-04-25T15:25:44.872200Z"
-    }
-   },
-   "id": "540df677d458c972",
-   "execution_count": 52
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/opendc-experiments/opendc-experiments-scenario/src/main/resources/bitbrains-small/interference-model.json b/opendc-experiments/opendc-experiments-scenario/src/main/resources/bitbrains-small/interference-model.json
deleted file mode 100644
index 51fc6366e..000000000
--- a/opendc-experiments/opendc-experiments-scenario/src/main/resources/bitbrains-small/interference-model.json
+++ /dev/null
@@ -1,21 +0,0 @@
-[
-    {
-        "vms": [
-            "141",
-            "379",
-            "851",
-            "116"
-        ],
-        "minServerLoad": 0.0,
-        "performanceScore": 0.8830158730158756
-    },
-    {
-        "vms": [
-            "205",
-            "116",
-            "463"
-        ],
-        "minServerLoad": 0.0,
-        "performanceScore": 0.7133055555552751
-    }
-]
diff --git a/opendc-experiments/opendc-experiments-scenario/src/main/resources/bitbrains-small/trace/meta.parquet b/opendc-experiments/opendc-experiments-scenario/src/main/resources/bitbrains-small/trace/meta.parquet
deleted file mode 100644
index 9cded35f4..000000000
Binary files a/opendc-experiments/opendc-experiments-scenario/src/main/resources/bitbrains-small/trace/meta.parquet and /dev/null differ
diff --git a/opendc-experiments/opendc-experiments-scenario/src/main/resources/bitbrains-small/trace/trace.parquet b/opendc-experiments/opendc-experiments-scenario/src/main/resources/bitbrains-small/trace/trace.parquet
deleted file mode 100644
index 9d953956b..000000000
Binary files a/opendc-experiments/opendc-experiments-scenario/src/main/resources/bitbrains-small/trace/trace.parquet and /dev/null differ
diff --git a/opendc-experiments/opendc-experiments-scenario/src/main/resources/env/multi.json b/opendc-experiments/opendc-experiments-scenario/src/main/resources/env/multi.json
deleted file mode 100644
index 721005b06..000000000
--- a/opendc-experiments/opendc-experiments-scenario/src/main/resources/env/multi.json
+++ /dev/null
@@ -1,66 +0,0 @@
-{
-    "clusters":
-    [
-        {
-            "name": "C01",
-            "hosts" :
-            [
-                {
-                    "name": "H01",
-                    "cpus":
-                    [
-                        {
-                            "coreCount": 32,
-                            "coreSpeed": 3200
-                        }
-                    ],
-                    "memory": {
-                        "memorySize": 256000
-                    }
-                }
-            ]
-        },
-        {
-            "name": "C02",
-            "hosts" :
-            [
-                {
-                    "name": "H02",
-                    "count": 6,
-                    "cpus":
-                    [
-                        {
-                            "coreCount": 8,
-                            "coreSpeed": 2930
-                        }
-                    ],
-                    "memory": {
-                        "memorySize": 64000
-                    }
-                }
-            ]
-        },
-        {
-            "name": "C03",
-            "hosts" :
-            [
-                {
-                    "name": "H03",
-                    "count": 2,
-                    "cpus":
-                    [
-                        {
-                            "coreCount": 16,
-                            "coreSpeed": 3200
-                        }
-                    ],
-                    "memory": {
-                        "memorySize": 128000
-                    }
-                }
-            ]
-        }
-    ]
-}
-
-
diff --git a/opendc-experiments/opendc-experiments-scenario/src/main/resources/env/single.json b/opendc-experiments/opendc-experiments-scenario/src/main/resources/env/single.json
deleted file mode 100644
index a1c8d95af..000000000
--- a/opendc-experiments/opendc-experiments-scenario/src/main/resources/env/single.json
+++ /dev/null
@@ -1,26 +0,0 @@
-{
-    "clusters":
-    [
-        {
-            "name": "C01",
-            "hosts" :
-            [
-                {
-                    "name": "H01",
-                    "cpus":
-                    [
-                        {
-                            "coreCount": 8,
-                            "coreSpeed": 3200
-                        }
-                    ],
-                    "memory": {
-                        "memorySize": 128000
-                    }
-                }
-            ]
-        }
-    ]
-}
-
-
diff --git a/opendc-experiments/opendc-experiments-scenario/src/main/resources/log4j2.xml b/opendc-experiments/opendc-experiments-scenario/src/main/resources/log4j2.xml
deleted file mode 100644
index e479f2ca9..000000000
--- a/opendc-experiments/opendc-experiments-scenario/src/main/resources/log4j2.xml
+++ /dev/null
@@ -1,43 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<!--
-  ~ MIT License
-  ~
-  ~ Copyright (c) 2020 atlarge-research
-  ~
-  ~ Permission is hereby granted, free of charge, to any person obtaining a copy
-  ~ of this software and associated documentation files (the "Software"), to deal
-  ~ in the Software without restriction, including without limitation the rights
-  ~ to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-  ~ copies of the Software, and to permit persons to whom the Software is
-  ~ furnished to do so, subject to the following conditions:
-  ~
-  ~ The above copyright notice and this permission notice shall be included in all
-  ~ copies or substantial portions of the Software.
-  ~
-  ~ THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-  ~ IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-  ~ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-  ~ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-  ~ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-  ~ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-  ~ SOFTWARE.
-  -->
-
-<Configuration status="WARN">
-    <Appenders>
-        <Console name="Console" target="SYSTEM_OUT">
-            <PatternLayout pattern="%d{HH:mm:ss.SSS} [%highlight{%-5level}] %logger{36} - %msg%n" disableAnsi="false"/>
-        </Console>
-    </Appenders>
-    <Loggers>
-        <Logger name="org.opendc" level="warn" additivity="false">
-            <AppenderRef ref="Console"/>
-        </Logger>
-        <Logger name="org.apache.hadoop" level="warn" additivity="false">
-            <AppenderRef ref="Console"/>
-        </Logger>
-        <Root level="error">
-            <AppenderRef ref="Console"/>
-        </Root>
-    </Loggers>
-</Configuration>
diff --git a/opendc-experiments/opendc-experiments-scenario/src/main/resources/scenario.json b/opendc-experiments/opendc-experiments-scenario/src/main/resources/scenario.json
deleted file mode 100644
index 3b22e5f4f..000000000
--- a/opendc-experiments/opendc-experiments-scenario/src/main/resources/scenario.json
+++ /dev/null
@@ -1,16 +0,0 @@
-{
-    "runs": 5,
-    "topology": {
-        "pathToFile": "resources/env/single.json"
-    },
-    "workload": {
-        "pathToFile": "resources/bitbrains-small",
-        "type": "ComputeWorkload"
-    },
-    "allocationPolicy": {
-        "policyType": "Mem"
-    },
-    "failureModel": {
-        "name": "TEST"
-    }
-}
diff --git a/opendc-experiments/opendc-experiments-scenario/src/test/resources/env/single.txt b/opendc-experiments/opendc-experiments-scenario/src/test/resources/env/single.txt
deleted file mode 100644
index 5642003d4..000000000
--- a/opendc-experiments/opendc-experiments-scenario/src/test/resources/env/single.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-ClusterID;ClusterName;Cores;Speed;Memory;numberOfHosts;memoryCapacityPerHost;coreCountPerHost
-A01;A01;8;3.2;128;1;128;8
-
diff --git a/opendc-experiments/opendc-experiments-scenario/src/test/resources/env/topology.txt b/opendc-experiments/opendc-experiments-scenario/src/test/resources/env/topology.txt
deleted file mode 100644
index 6b347bff4..000000000
--- a/opendc-experiments/opendc-experiments-scenario/src/test/resources/env/topology.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-ClusterID;ClusterName;Cores;Speed;Memory;numberOfHosts;memoryCapacityPerHost;coreCountPerHost
-A01;A01;32;3.2;2048;1;256;32
-B01;B01;48;2.93;1256;6;64;8
-C01;C01;32;3.2;2048;2;128;16
-
diff --git a/opendc-experiments/opendc-experiments-scenario/src/test/resources/trace/bitbrains-small/interference-model.json b/opendc-experiments/opendc-experiments-scenario/src/test/resources/trace/bitbrains-small/interference-model.json
deleted file mode 100644
index 51fc6366e..000000000
--- a/opendc-experiments/opendc-experiments-scenario/src/test/resources/trace/bitbrains-small/interference-model.json
+++ /dev/null
@@ -1,21 +0,0 @@
-[
-    {
-        "vms": [
-            "141",
-            "379",
-            "851",
-            "116"
-        ],
-        "minServerLoad": 0.0,
-        "performanceScore": 0.8830158730158756
-    },
-    {
-        "vms": [
-            "205",
-            "116",
-            "463"
-        ],
-        "minServerLoad": 0.0,
-        "performanceScore": 0.7133055555552751
-    }
-]
diff --git a/opendc-experiments/opendc-experiments-scenario/src/test/resources/trace/bitbrains-small/meta.parquet b/opendc-experiments/opendc-experiments-scenario/src/test/resources/trace/bitbrains-small/meta.parquet
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diff --git a/opendc-experiments/opendc-experiments-scenario/src/test/resources/trace/bitbrains-small/trace.parquet b/opendc-experiments/opendc-experiments-scenario/src/test/resources/trace/bitbrains-small/trace.parquet
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