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CSC458 Programming Assignment 2: Bufferbloat

 CSC458 Programming Assignment 2: Bufferbloat 

Introduction 
In this exercise we will study the dynamics of TCP in home networks. Figure 1 shows a “typical” home network with a 
Home Router connected to an end host. The Home Router is connected via Cable or DSL to a Headend router at the 
Internet access provider’s office. We are going to study what happens when we download data from a remote server to 
the End Host in this home network. 
Figure 1. Typical home network setup 
In a real network it’s hard to measure TCP’s congestion windows size (i.e., cwnd, because it’s private to the Server) and 
the buffer occupancy (because it’s private to the router). To make our measurement job easier, we are going to emulate 
the network in Mininet. 
Goals 
 Learn first‐hand the dynamics of TCP sawtooth and router buffer occupancy in a network. 
 Learn why large router buffers can lead to poor performance. This problem is often called “bufferbloat.” 
 Learn how to use Mininet to run traffic generators, collect statistics and plot them. 
 Learn how to package your experiments so it’s easy for others to run your code. 
Setup 
Within Mininet, create the topology that is shown in Figure 2: h1 represents your home computer that has a fast 
connection (1Gb/s) to your home router (s0) which has a slow uplink connection (10Mb/s). h2 represents a server that 
you want to download a file from. The round‐trip propagation delay, or the minimum RTT between h1 and h2 is 4ms. 
The router buffer size can hold 100 full sized ethernet frames (about 150kB with an MTU of 1500 bytes). 
Figure 2. Assignment topology 
Tasks 
We want to perform the following tasks simultaneously using different queue sizes at the router: 
 Start a long‐lived TCP flow sending data from h1 to h2. Use iperf. We will record the congestion windows size 
(cwnd) of this connection. 
 Send 10 pings per second from h1 to h2 and record their RTTs. 
 Spawn a webserver on h1. Download the index.html web page from h1 repeatedly every two seconds and 
measure how long it takes to fetch it (on average). The starter code has some hints on how to do this. Make sure 
that the webpage download data is going in the same direction as the long‐lived flow (h1 to h2). You should use 
the curl command to download the page. 
 These three tasks (long‐lived flow, ping train, and webserver downloads) should all be happening 
simultaneously.  
 Plot the time‐series of the following: 
o The long‐lived TCP flow’s cwnd
o The RTT reported by ping 
o Webpage download time 
o Queue size at the router 
You should perform this simulation using three different queue sizes: Q=5 packets, Q=20 packets, and Q=100 packets. 
Starter Code 
To help you get started, we provide a basic skeleton code inside a virtual machine. Once you have your instance running, 
run a Mininet sanity check (sudo mn ‐‐test pingall) and then continue with this assignment. Your instance should have 
Mininet and most tools pre‐installed. 
Download Instructions 
Download the Mininet VirtualBox VM from here: 
http://www.cs.toronto.edu/~yganjali/bb/cs244‐vm.ova
After importing it into VirtualBox (or any other VM software that you prefer to use), use the username “cs244” and 
password “cs244” to sign into the VM. Then clone the starter code for this project from this repository: 
https://github.com/Network‐Lab/cs244‐bufferbloat.git
You can do this by running the following command in the VM: 
> git clone https://github.com/Network-Lab/cs244-bufferbloat.git 
This will create a new folder named “cs244‐bufferbloat” that contains the starter code. Look for TODOs in the starter 
code. The following are important files you will find in the repository. Ignore other files. 
 bufferbloat.py: Creates the topology, measures cwnd, queue sizes and RTTs and spawns a webserver. 
 plot_queue.py: Plots the queue occupancy at the bottleneck router. 
 plot_ping.py: Parses and plots the RTT reported by ping. 
 plot_tcpprobe.py: Plots the cwnd time‐series for a flow specified by its destination port 
 run.sh: Runs the experiment and generates all graphs in one go. 
Note: We recommend you use a version control system to track changes to your assignment and back it up regularly. 
Both Github and Bitbucket provide private git repositories for free. Be careful to NOT use a public repository. 
Submission/Deliverables 
This assignment is to be completed individually. Submit your work on MarkUS 
(https://markus.teach.cs.toronto.edu/csc458‐2020‐09).  
Due date for this assignment is Sunday, November 29, 2020 at 11:59pm. 
This assignment worth 18% of your total term mark. You should submit three items: a written report (10%), raw 
generated plots (4%), and your code (4%). 
Written Report 
You should prepare a 2‐5 pages report (with text font size of 12 points). The report should provide a brief description of 
the bufferbloat problem and its impact on the users. Then it should describe (briefly) the simulation setup that you had. 
These two parts together should not be more than 50% of the total report. The main part of the report will present your 
results (graphs) and discuss what it means. It should answer the following questions: 
 Why do you see a difference in webpage fetch times with short and large router buffers? 
 Bufferbloat can occur in other places such as your network interface card (NIC). Check the output of ifconfig 
eth0 on your VirtualBox VM. What is the (maximum) transmit queue length on the network interface reported 
by ifconfig? For this queue size, if you assume the queue drains at 100Mb/s, what is the maximum time a packet 
might wait in the queue before it leaves the NIC? 
 How does the RTT reported by ping vary with the queue size? Write a symbolic equation to describe the relation 
between the two (ignore computation overheads in ping that might affect the final result). 
 Identify and describe two ways to mitigate the bufferbloat problem. 
The report should be named “pa2.pdf”. It should be an electronic and typed document (e.g., not a hand‐written and 
scanned pdf). In addition to be marked for correct technical content (e.g., answering those questions), the presentation 
is also being marked (e.g., have separate sections for different topics, having a reference section, …). Overall, the report 
should be similar to a technical reports and conference papers.
Generated Graphs 
You should 12 graphs that you code generates. There will be 4 charts for each queue size (rtt, cwnd, queue size, and 
download time) and 3 different queue sizes (5, 20, 100). Your file names should be: 
 RTT graphs: rtt‐5.png, rtt‐20.png, rtt‐100.png
 cwnd size: cwnd‐5.png, cwnd‐20.png, cwnd‐100.png
 Queue size: q‐5.png, q‐20.png, q‐100.png
 Download time: download‐5.png, download‐20.png, download‐100.png
The 5% mark for this part focuses on the correctness of the graphs. 
Note that these graphs should be identical to the ones that anyone else (including TAs) can generate by simply running 
your code (run.sh). I.e., these should be the raw output of running your code. 
Code 
You should upload the 5 main scripts that you need to update to generate the graphs: bufferbloat.py, plot_queue.py, 
plot_ping.py, plot_tcpprobe.py, and run.sh
This 5% mark of this part will focus on your coding style and documentation, similar to the first programming 
assignment. 
Useful Hints 
 To look at your output, you may find it useful to start a web server using python ‐m SimpleHTTPServer. Start the 
webserver in the cs244‐bufferbloat directory. Then you can point a web browser to :8000 and browse your 
results. 
 If your Mininet script does not exit cleanly due to an error (or if you pressed Control‐C), you may want to issue a 
clean command before you start Mininet again: 
> sudo mn -c 
Useful Links 
 On the bufferbloat problem" 
o https://netduma.com/blog/beginners‐guide‐to‐bufferbloat/
o https://www.bufferbloat.net/projects/bloat/wiki/Introduction/
 More about Mininet and how it can be used: 
o http://mininet.org/walkthrough/
o http://geekstuff.org/2018/09/26/mininet‐tutorial/
 Monitoring and Tuning the Linux Networking Stack: 
o https://blog.packagecloud.io/eng/2016/06/22/monitoring‐tuning‐linux‐networking‐stack‐receiving‐
data/
o https://blog.packagecloud.io/eng/2016/10/11/monitoring‐tuning‐linux‐networking‐stack‐receiving‐
data‐illustrated/
 
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