This repository contains a collection of scripts used to run the experiemnts and generate the figures in our SIGCOMM 2018 paper: Restructuring Endpoint Congestion Control.
To make the process of reproducing our results easier, this repository contains
a Vagrantfile that tells Vagrant how to setup a machine running the proper
version of Linux with all of the proper dependencies. Simply install
Vagrant and then run vagrant up. This will
create a new Linux VM with this directory (eval-scripts/) linked as /ccp
inside the VM. You can access the VM by running vagrant ssh.
If you already have a Linux machine or VM and you do not wish to use Vagrant you can simply clone this
repository and run the ccp-system-setup.sh. All of our experiments were run on
a machine with Ubuntu 17.10 (Linux 4.13). We rely on tcpprobe for congestion window instrumentation, which was removed in the latest Ubuntu LTS release (18.04 -> kernel 4.15). Therefore unfortunately we cannot support these scripts on newer kernels.
Once you have a machine setup, simply running make inside this directory (or /ccp
inside the Vagrant VM) should build all of the necessary components.
Below is a list of all figures in the paper and the commands necessary to run the corresponding experiment.
- Load ccp-kernel:
cd ccp-kernel && sudo ./ccp_kernel_load ipc=0 - Start BBR:
cd bbr && sudo ./target/release/bbr --ipc=netlink - Start an iperf server:
iperf -s -p 5000 - Start an iperf client with ccp inside mahimahi and with logging:
mm-delay 10 mm-link --cbr 48M 48M --uplink-queue="droptail" --downlink-queue="droptail" --uplink-queue-args="packets=160" --downlink-queue-args="packets=160" --log=bbr.log iperf -c $MAHIMAHI_BASE -p 5000 -t 30 -i 1 -Z ccp - Graph the result:
mm-graph mahimahi.log 20
This process is automated in the following script.
See the fidelity target in the Makefile and modify to your preference. This script can take quite a while to run, and generates a lot of output, so by default the Makefile target does not run the same experiments as described in the paper.
The relevant arguments to the run-fidelity-exp.py script are:
--alg=(cubic|reno)for Cubic and Reno--scenario=(fixed|cell|drop)for the three scenarios described in Section 7.1.1 of the paper--itersfor the number of iterations to run each experiment. As Section 7.1.1 describes, we use a value of 20. However, with this value the*-*-cwndevo.pdfgraphs become quite large in size: you may have to modify the scripts (perhaps by modifying the sampling on line 105 of run-fidelity-exp.py) to view the results efficiently.
For Figure 9, pick one run for cubic for each of the above and run
./plot/cwnd-evo-single.r fidelity/cwndevo-subsampled.log cubic fixed <ccp_iteration> <kernel_iteration> <output_file> light
make ipc
python3 fct_scripts/fct_exp.py
./scripts/run-scalability-exp.py
The experiments corresponding to Figures 7, 8, and 14 require significant setup external to CCP. As a result we do not currently support them. Please contact us for further information.