MANO Benchmarking Framework (MBF) is a result of a small script that was used to run the experiments as part of the pg-scramble project. The idea of MBF is to provide MANO developers with a generic framework for running experiments on MANO. MBF mainly provides the following

1. Easy interfacing with MANO instances by using python-mano-wrappers
2. Ability to run experiments with different service descriptors
3. Collection of performance metrics in convenient data format
4. Flexible graphing mechanism of the collected data.

1. Setup regular pishahang installation

2. Install netdata on the same vm using the following commands

    sudo su
   
    apt-get install zlib1g-dev uuid-dev libuv1-dev liblz4-dev libjudy-dev libssl-dev libmnl-dev gcc make git autoconf autoconf-archive autogen automake pkg-config curl python
   
    git clone --branch v1.19.0 https://github.com/netdata/netdata.git --depth=100
    cd netdata
   
    ./netdata-installer.sh
   
    # use browser and see <IP>:19999 to verify
   

3. Install supporting flask server to fetch metrics like deployment times and cleanup operations

    git clone https://github.com/CN-UPB/MANO-Benchmarking-Framework.git
    cd MANO-Benchmarking-Framework/experiment-runner/
   
    # Run flask server
    ./start_flask_helper_server.sh
   

Detailed info here

    git clone https://github.com/CN-UPB/MANO-Benchmarking-Framework.git
    cd MANO-Benchmarking-Framework
    cd vim-mocker

    ./build_run_docker_pishahang_os_remote.sh

    cd MANO-Benchmarking-Framework/experiment-runner

    # build experiment container
    ./build_experiment_runner.sh

    # run experiment container
    ./run_experiment_runner.sh

Once terminal is open inside the container, run one of the following experiment

    python run-emu-experiment-pishahang.py
    python run-emu-experiment-osm.py

    # or in background to avoid process from quitting after disconnection

    nohup python -u ./run-emu-experiment-sonata.py > emu-experiment.log &

• Data is stored in the EXP_RESULTS folder with the given name

• Parsing and plotting are done with the files csv-result-parser-rpm-v2.py and plot-graphs-v2.py

# To show logs 
sudo docker logs experiment-runner -f

# This is for testing scaling plugin

• Enter vim address as vim-mocker.
• Other details can be anything