Running PINT on Mininet

This repository gives the sample code to run and test PINT on Mininet.

Requirements

1. Mininet supporting bmv2 (https://github.com/nsg-ethz/p4-learning)
2. networkx (pip install networkx)
3. scapy (pip install scapy)
4. numpy (pip install numpy)

Steps to run PINT

• Create topology.

Ensure you are running this in VM with Mininet. Create a Mininet topology to conduct path tracing on path size N.

python topo_allocator.py 5

where 5 indicates that path tracing needs to be conducted on five switches. In our paper, we used N= 5, 36, 59.

• Start Mininet.

Start Mininet with the newly constructed topology.

sudo p4run --config p4app.json

• Start path tracing.

Start path tracing by specifying the length of path (N).

sudo python exp.py 5

where 5 indicates the length of path.

• Generate results.

Generate results using:

python generate_results.py 5

where 5 indicates the length of path. The results can be found under final_results/5. There will be three files, indicating the average, median and tail number of packets required to conduct path tracing for path length of 5. This will also contain results for ASM and PPM techniques.

Running PINT for delay quantiles

Requirements

1. Python 3.7.5
2. numpy (pip install numpy)

Setting up Python 3.7.5 in Mininet VM

• Install Miniconda: https://docs.conda.io/en/latest/miniconda.html
• Create a new Python 3.7.5 environment.
• Run PINT for delay quantiles in that environment.

Steps to run PINT for delay quantiles

• Generate delay data obtained from ns3 simulations.

python generate_delay_data.py file_name

where file_name is the location of delay data generated from ns3 simulations. A sample processed data is present in experiments/delays/processed_data

• Generate results

python generate_delay_results.py

This generates the average, median and tail latencies in final_results/delays.