Teaching Computer Networks with Reproducible Research

Lead Mentor: Fraida Fund

In the field of computer networks and wireless communication systems, the availability of open access networking and cloud computing testbeds (GENI, CloudLab, Chameleon, FABRIC, and others) has been transformative in promoting reproducible research and in making high-quality experiential learning available to students and educators at a wide range of colleges and universities. This project seeks to unite research and education use of these testbeds by developing new ways of using reproducible research to teach computer networks and related topics.

Bringing foundational results into the classroom

  • Topics: Computer networks, reproducibility, education
  • Skills: Linux, writing
  • Difficulty: Medium
  • Size: 350 hours
  • Mentor(s): Fraida Fund and TBD

To make foundational results from computer networks more concrete, this project seeks to reproduce a selection of key results and package them for use as interactive classroom demonstrations. (An example of a “foundational” result might be the result from the 1980s that motivates congestion control by showing how congestion collapse occurs when the network is under heavy load.) This involves:

  • Reproducing the original results on an open-access testbed
  • Packaging the materials for use as a classroom demo, with interactive elements
  • Creating assessment questions and sample “solutions” related to the materials, that instructors may use in homework assignments or exams

Developing a “classroom competition” for adaptive video delivery policies

  • Topics: Computer networks, adaptive video, reproducibility, education
  • Skills: Linux, Python, writing
  • Difficulty: Medium
  • Size: 350 hours
  • Mentor(s): Fraida Fund and TBD

A carefully designed competition can be a fun and exciting way for students to challenge themselves and gain “ownership” of a new topic. This projects builds on an existing open source reproducible result for adaptive video delivery, and will challenge students to extend this work and design their own adaptive video policies for head-to-head competition against their classmates. This includes:

  • Packaging the result to make it easier for students to reproduce and then build on the original work
  • Implementing other adaptive video policies from the literature, so that students can use them as a baseline
  • Developing different network settings (using live link traces and emulated link patterns) in which student submissions may be evaluated
  • Developing an evaluation framework for scoring student submissions on different criteria and in different network settings, and making the results available in a leaderboard format