Merge pull request #276 from Tencent/release/v0.12.0

Release/v0.12.0

.github/workflows/plot-benchmark.py

@@ -0,0 +1,176 @@
+#!/usr/bin/env python3
+
+import os
+import sys
+
+import datetime
+import matplotlib.pyplot as plt
+import numpy as np
+import prettytable
+import termcolor
+
+# Benchmark scenarios
+SCENARIO = ["long", "short"]
+
+# QUIC implementations.
+# The first element is used as the normalization base.
+IMPLS = ["lsquic", "tquic"]
+
+# Round of benchmark in one scenario.
+ROUND = 5
+
+# File sizes in long connection scenario benchmark.
+LONG_FILE_SIZES = ["15K", "50K", "2M"]
+
+# File sizes in short connection scenario benchmark.
+SHORT_FILE_SIZES = ["1K"]
+
+# Different concurrent connections.
+LONG_CONNS = [10]
+
+# Different concurrent connections.
+SHORT_CONNS = [10]
+
+# Different concurrent streams.
+LONG_STREAMS = [1, 10]
+
+# Different concurrent streams.
+SHORT_STREAMS = [1]
+
+# Time span of the trend chart.
+DAYS = 90
+
+# Read data from benchmark result file.
+def read_data(data_dir, scen, impl, size, conn, stream, round, date):
+    dirname = "benchmark_%s_%s_%d_%d.%s" % (scen, size, conn, stream, date)
+    filename = "benchmark_%s_%s_%s_%d_%d.%d.%s" % (scen, impl, size, conn, stream, round, date)
+    path = os.path.join(data_dir, dirname, filename)
+    try:
+        with open(path) as f:
+            data = f.read().strip()
+            return float(data)
+    except:
+        return 0.0
+
+# Load benchmark results into array.
+def prepare_data(data_dir):
+    titles = [' ' for _ in range((len(LONG_FILE_SIZES)*len(LONG_CONNS)*len(LONG_STREAMS) + len(SHORT_FILE_SIZES)*len(SHORT_CONNS)*len(SHORT_STREAMS)))]
+    result = [[[[0.0 for _ in range(len(LONG_FILE_SIZES)*len(LONG_CONNS)*len(LONG_STREAMS) + len(SHORT_FILE_SIZES)*len(SHORT_CONNS)*len(SHORT_STREAMS))] for _ in range(len(IMPLS))] for _ in range(DAYS)] for _ in range(ROUND) ]
+
+    # Load long connection scenario result.
+    I = len(LONG_FILE_SIZES)
+    J = len(LONG_CONNS)
+    K = len(LONG_STREAMS)
+    N = len(IMPLS)
+    D = DAYS
+    for i in range(I):
+        for j in range(J):
+            for k in range(K):
+                titles[i*J*K+j*K+k] = "long %s %d %d" % (LONG_FILE_SIZES[i], LONG_CONNS[j], LONG_STREAMS[k])
+                for n in range(N):
+                    for d in range(D):
+                        for r in range(ROUND):
+                            date = (datetime.datetime.now() - datetime.timedelta(days=d)).strftime('%Y-%m-%d')
+                            result[r][D-1-d][n][i*J*K+j*K+k] = read_data(data_dir, "long", IMPLS[n], LONG_FILE_SIZES[i], LONG_CONNS[j], LONG_STREAMS[k], r, date)
+
+    # Load short connection scenario result.
+    M = len(LONG_FILE_SIZES)*len(LONG_CONNS)*len(LONG_STREAMS)
+    I = len(SHORT_FILE_SIZES)
+    J = len(SHORT_CONNS)
+    K = len(SHORT_STREAMS)
+    N = len(IMPLS)
+    D = DAYS
+    for i in range(I):
+        for j in range(J):
+            for k in range(K):
+                titles[M+i*J*K+j*K+k] = "short %s %d %d" % (SHORT_FILE_SIZES[i], SHORT_CONNS[j], SHORT_STREAMS[k])
+                for n in range(N):
+                    for d in range(D):
+                        for r in range(ROUND):
+                            date = (datetime.datetime.now() - datetime.timedelta(days=d)).strftime('%Y-%m-%d')
+                            result[r][D-1-d][n][M+i*J*K+j*K+k] = read_data(data_dir, "short", IMPLS[n], SHORT_FILE_SIZES[i], SHORT_CONNS[j], LONG_STREAMS[k], r, date)
+
+    # Average by rounds.
+    result_avg = np.mean(np.array(result), axis=0).tolist()
+
+    # Normalize benchmark result.
+    for d in range(D):
+        base = result_avg[d][0]
+        for i in range(1, len(result_avg[d])):
+            result_avg[d][i] = [round(x/y, 4) if y != 0 else 0 for x, y in zip(result_avg[d][i], base)]
+        for i in range(len(result_avg[d][0])):
+            if result_avg[d][0][i] != 0:
+                result_avg[d][0][i] = 1
+
+    return titles, result_avg
+
+# Print benchmark performance result to stdout.
+def show(titles, result):
+    table = prettytable.PrettyTable()
+    table.field_names = titles
+
+    for i in range(len(result)):
+        colored_row_name = termcolor.colored(IMPLS[i], 'green')
+        table.add_row([colored_row_name] + result[i])
+
+    print(table)
+
+# Plot graph according to benchmark performance result.
+def plot(titles, result):
+
+    N = len(titles)
+    M = len(result)
+
+    width = 0.35
+    gap = 0.5
+
+    ind = np.arange(N) * (width * M + gap)
+
+    fig, ax = plt.subplots()
+    fig.set_size_inches(10, 5)
+    for i in range(M):
+        ax.bar(ind + i*width, result[i], width, label=IMPLS[i])
+
+    ax.set_ylabel('RPS')
+    ax.set_title('TQUIC benchmark')
+    ax.set_xticks(ind + width * M / 2)
+    ax.set_xticklabels(titles, rotation=45, fontsize=6)
+
+    ax.legend()
+
+    plt.savefig("benchmark_all.png", dpi=300)
+
+# Plot trend of latest days.
+def trend(titles, result):
+    num_scenarios = len(result[0][0])
+    num_curves = len(result[0])
+
+    fig = plt.figure(figsize=(10, num_scenarios*5)) 
+
+    for s in range(num_scenarios):
+        ax = fig.add_subplot(num_scenarios, 1, s+1)
+        ax.set_title(titles[s])
+        ax.set_xlabel("Date")
+        ax.set_ylabel("RPS")
+
+        for c in range(num_curves):
+            y_values = [result[d][c][s] for d in range(DAYS)]
+            ax.plot(list(range(DAYS)), y_values, label=IMPLS[c])
+
+        ax.legend()
+
+    plt.tight_layout()
+    plt.savefig("benchmark_all_trend.png", dpi=300)
+
+if __name__ == '__main__':
+    if len(sys.argv) < 2:
+        print("Usage: %s [data_dir]" % (sys.argv[0]))
+        exit(1)
+
+    data_dir= sys.argv[1]
+    titles, result = prepare_data(data_dir)
+    plot(titles, result[DAYS-1])
+    trend(titles, result)
+    titles.insert(0, '')
+    show(titles, result[DAYS-1])
+

.github/workflows/plot-fct.py

@@ -7,7 +7,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
-# QUIC implementes
+# QUIC implementations
 IMPLS = ["tquic", "gquiche", "lsquic", "picoquic", "quiche"]
 
 # Different modes

.github/workflows/plot-goodput.py

@@ -7,7 +7,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
-# QUIC implementes
+# QUIC implementations
 IMPLS = ["tquic", "gquiche", "lsquic", "picoquic", "quiche"]
 
 # Different file sizes

.github/workflows/plot-interop.py

@@ -8,7 +8,7 @@
 import numpy as np
 from matplotlib.colors import ListedColormap
 
-# QUIC implementes
+# QUIC implementations
 CLIENT_IMPLS = ["tquic", "lsquic", "quiche", "picoquic", "ngtcp2", "msquic",
                 "s2n-quic", "quinn", "neqo", "kwik", "aioquic", "chrome",
                 "go-x-net", "quic-go", "mvfst"]