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Generic Placement Emulation Framework

This placement emulation framework (PEF) supports the automatic emulation of network service placements calculated by arbitrary placement algorithms. This allows to get hands-on practical experience with deployed services and enables realistic performance measurements.

This prototype belongs to the paper "A Generic Emulation Framework for Reusing and Evaluating VNF Placement Algorithms" accepted at IEEE NFV-SDN 2018 by Stefan Schneider, Manuel Peuster, and Holger Karl.

Cite this work

If you use this placement emulation framework, please cite the following publication:

Stefan Schneider, Manuel Peuster, and Holger Karl: "A Generic Emulation Framework for Reusing and Evaluating VNF Placement Algorithms", 2018 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), Verona, Italy. (2018)

Installation

  1. Install the emulator vim-emu (see README.md "Bare-metal installation")
  • cd vim-emu; sudo python setup.py develop
  1. Pre-build VNF containers: cd inputs/vnfs; ./build.sh
  2. Install the B-JointSP placement algorithm bjointsp 2.3+
  3. Install other dependencies of the placement emulation framework: python setup.py install (requires Python 3.5+)

We tested the installation on Ubuntu 16.04.

To install the dependencies required for running the Jupyter notebooks, additionally run:

pip install -r notebook_requirements.txt

Usage

Quickstart:

To start the emulator, calculate the placement, emulate it, measure and log delays, and stop the emulator with one command, use:

./place-emu.sh -a algorithm -n network -t service -s sources -c num_pings

Where algorithm is a placement algorithm, e.g., bjointsp, random, or greedy. Inputs network, service, and sources are paths to input parameters, e.g., from the inputs. num_pings is the number of packets sent by each delay measurement, e.g., 5 for testing and 100 for evaluation. If you just want to test placement-emulation with predefined parameters, simply run test.sh.

Separate steps:

If you prefer to run the steps yourself, you can follow these manual steps:

  1. Select inputs from inputs
  2. Start a network topology on vim-emu, e.g., sudo python place_emu/emulator/topology_zoo.py -g inputs/networks/Abilene.graphml
  3. Start the placement and emulation with python3 place_emu/placement_emulator.py -a bjointsp --network inputs/networks/Abilene.graphml --service inputs/services/fw1chain.yaml --sources inputs/sources/source0.yaml.
  4. You can test the deployment and connectivity as described in the wiki, e.g., with vim-emu compute list. Delay measurements can be performed with ping or httping from inside vim-emu.
  5. Important: Stop the emulator using exit inside the ContainerNet terminal. This is necessary to clean up, so that the emulator can be started again.

Note: The -a argument sets the placement algorithm: Currently, we support bjointsp, random, and greedy. If you only want to trigger placement without emulation, use the --placeOnly option when calling placement_emulator.py.

Experiments:

Use scripts like runAll.sh to run a large number of placement emulations sequentially. Important: use |& tee some_log.log to log the display output for debugging.

Integrating a placement algorithm to the standard interface

To integrate your VNF placement algorithm to the standard interface of the placement abstraction layer, you only have to adjust the inputs, outputs, and triggering of your algorithm. For examples, see the B-JointSP scaling and placement algorithm or the two dummy algorithms in this repo: Greedy and random placement.

Currently, the framework is implemented as a Python library. In the future, we plan on implementing a REST API, which will allow interacting with the interface independent of the programming language.

Triggering

Your algorithm needs to implement a function place() that triggers the placement:

def place(network_file, service_file, sources_file, optional_args=None):
    # start placement
    return placement

You can use arbitrary optional_args depending on the needs of your algorithm.

Inputs

Currently, the mandatory inputs are formatted YAML files (the structure may evolve as we continue improving the framework). See the examples here.

Outputs

The output of your algorithm is the returned placement (the return value from place()). This placement is a Python dictionary (similar to a YAML file) that needs to have at least the following fields: A placement dictionary with vnfs and vlinks. vnfs is a list of the placed VNFs with name and location (and image if you want to use the emulator). vlinks is a list of vLinks connecting the placed VNFs.

Of course, you can add any additional information in the dictionary that may be useful for the evaluation.

Contributions

We plan on continuously improving this framework and contributions are very welcome!

Please ask for new features in the issues or submit new features in the form of pull requests.

Contact

Lead developers: Stefan Schneider and Manuel Peuster

For questions or support, please use GitHub's issue system. Please also consider the additional information on the wiki pages.