Srishti Jaiswal | UCSC OSPO

Final Blog on Teaching Computer Networks with Reproducible Research: Developing a 'classroom competition' for adaptive video delivery

Fri, 20 Oct 2023 00:00:00 +0000

Hello Again!

I’m excited to present my final blog post summarizing the progress and achievements made over the 2023 Summer of Reproducibility Fellowship.I will be sharing the work I’ve created for the Teaching Computer Networks with Reproducible Research: Developing a ‘classroom competition’ for adaptive video delivery.

Recap of the Journey

In my mid-term evaluation, I discussed the initial milestones and challenges I encountered during this program. At that point, I studied the key figures from the research paper ‘Downton Abbey Without the Hiccups: Buffer-Based Rate Adaptation for HTTP Video Streaming’. My primary objectives were to ensure compatibility with both Python 2 and Python 3 and to incorporate an ‘Estimated Download Rate’ metric into the output file generated by the adaptive video client. Furthermore, I expanded the project to include two crucial visualizations: buffer occupancy vs. time and estimated download rate vs. time.

Final Project Progress

In the final weeks of my internship, I worked towards my ultimate goal, which was to reproduce existing work and create a clear guide for future students. I aimed to enable them to build upon and improve this work. To achieve this, I created a new experiment using an existing one,

which I titled “Compare Adaptive Video Policies”

This experiment compares two policies: rate-based (basic) policy and buffer-based (Netflix) policy. In the experiment, I covered the following key aspects:

How Both Policies Work: I detailed the workings of both the rate-based and buffer-based policies, explaining how each policy selects the next bitrate, among other relevant information.

Instructions for Execution of Policies: After conducting several experiments with different settings, I determined the most appropriate settings for this experiment. These settings have been added to the instructions for executing both policies, with a focus on ensuring similar “high” network rates, “low” data rates, similar durations of the “high” data rate before the interruption, and similar durations of the “interruption.” This setup allows for an easy and clear comparison of the two policies.

Discussion Part: In the discussion section, I addressed the differences that students can observe after conducting the experiment and visualising the graphs and videos.

In conclusion, I would like to thank my mentor, Fraida Fund, who has given me excellent guidance and would like to express my gratitude to OSRE23, where I have learned so much. This experience has been amazing for my personal and professional growth.

Mid-term blog post for Teaching Computer Networks with Reproducible Research: Developing a 'classroom competition' for adaptive video delivery

Tue, 01 Aug 2023 00:00:00 +0000

Hello!

I am Srishti Jaiswal and this is my second blog post for the 2023 Summer of Reproducibility Fellowship.

Introduction

As I reach the halfway mark of my internship journey, I have had the incredible opportunity to work on a project that revolves around reproducing an adaptive video research result using cloud-based experimentation. This blog post delves into my exciting work so far, the significant milestones achieved, specific accomplishments to celebrate, and the challenges overcome. Utilizing CloudLab and FABRIC, I embarked on a journey to reproduce essential figures from the research paper Downton Abbey Without the Hiccups: Buffer-Based Rate Adaptation for HTTP Video Streaming, ensure Python2 and Python3 compatibility and incorporate an Estimated Download Rate column in the log file produced by the video client. Let’s explore the details of this captivating internship experience.

Major Milestones Reached

Here are the milestones we have reached so far:

Familiar with CloudLab and Fabric Testbeds: I learned how to run an adaptive video experiment, which is the jumping-off point for my project, on the CloudLab and FABRIC platforms.
Python2 and Python3 Compatibility: My first task was to port an existing open-source code base developed for Python2 (which is no longer supported) so that it can run in Python3. Now code is running successfully in both versions for all the policies of the existing open source, i.e. Basic, Netflix and Sara. Fixed issue#1 .
Estimated Download Rate for Basic Policy: To make it easier for users to understand and visualize how the adaptive video policy works, I added an additional metric, “Estimated Download Rate”, to the output file produced by the adaptive video client.
Graphing Buffer Occupancy and Estimated Download Rate: I extended the existing experiment to show two additional visualizations that are important for understanding how the adaptive video client works: buffer occupancy vs time and estimated download rate vs time.

Overcoming Challenges

I encountered several challenges throughout this project, especially as it was my first time working independently on a research paper as a third-year engineering student. However, with my mentor’s guidance and support, I persevered and learned to tackle each obstacle with determination.

One significant challenge was porting the entire code from Python2 to Python3. This transition resulted in numerous errors, and I often found it challenging to pinpoint where the mistakes occurred. To overcome this, I adopted a step-by-step approach, fixing errors one by one and verifying them using Python2 for comparison.

Understanding the complex codebase was another hurdle that led to moments of feeling stuck in an infinite loop. But every time I faced such situations, I sought my mentor’s advice, and together, we made strategic changes to overcome these challenges.

I am immensely grateful for my mentor’s expertise and support throughout this internship. Her guidance played a crucial role in helping me navigate through the challenges and grow both professionally and personally. I eagerly look forward to the rest of the journey, knowing that I can continue making meaningful contributions to this research project with her inspiring mentorship.

Future Prospects

As the second half of my internship approaches, I am eager to refine further and expand our experimentation. Our main aim is to reproduce the existing work and provide a clear guide for other students to do the same for this, I have to create a framework that helps them improve and build upon this work.

I hope you enjoyed reading this blog post.If you have any questions or feedback, please feel free to contact me. Thank you for your attention and stay tuned for more updates!

Teaching Computer Networks with Reproducible Research: Developing a 'classroom competition' for adaptive video delivery

Tue, 23 May 2023 00:00:00 +0000

As part of the Teaching Computer Networks with Reproducible Research project my proposal under the mentorship of Fraida Fund aims to develop a classroom competition for adaptive video delivery policies, leveraging an existing open-source reproducible result. The competition will challenge students to extend the original work and design their adaptive policies for head-to-head competition against their classmates.The project will involve packaging the existing result for easy reproducibility and building on it by implementing other adaptive video policies from the literature, developing different network settings for evaluating student submissions, and creating an evaluation framework for scoring submissions based on various criteria (so that competition remains fair and unbiased). The deliverables include a functional submission and evaluation process, an evaluation framework, and documentation and materials for course instructors to use in the classroom.