Interactive and Repeatable Experiments on Chameleon with Jupyter Notebook

As another semester begins, we’ve rounded up a series of fully packaged experiments on Chameleon. These experiments are all publicly available on Trovi, Chameleon’s sharing platform. The experiments can be used for classes to introduce different topics, tools, or datasets, or serve as an introduction to provisioning resources on Chameleon with Jupyter Notebook. Once you launch an experiment, you can edit the notebook, allowing individual experimentation, and letting you introduce variation, such as trying different resources or datasets.

2020 End-of-year Changelog

We'd like to take a brief moment to reflect with gratitude on what went right this year, at least in the (small) realm of our work on this research testbed. So here’s one year in review, representing combined “changelog” information from the entire year.

Tickets of the Year: Solutions to Your 2020 (Ticket) Problems

Is your instance not launching? Are your Floating IPs drifting aimlessly through the ether? Do you have a PI eligibility request? Chameleon tickets are the fastest way to reach the Chameleon support team and receive assistance for all your testbed needs. It’s 2020. Everyone could use a little extra help. 

As 2021 and Oscars season approaches, the Chameleon team has compiled “Tickets of the Year” designed to help you avoid (at least some of) the same stumbling blocks of 2020. Read on to learn about some of the most common tickets, their solutions, and some special ticket award categories. You can always reach out to the Help Desk team for white-glove troubleshooting help. 

Trovi: the Google Drive for Chameleon Experiments

Trovi is the next iteration of the Chameleon experiment management and sharing platform. With Trovi, you can set up and configure your experimental environment from within a Jupyter notebook, document and save your experiment similarly in notebook form, and privately share it with collaborators or publish it for any Chameleon user to build on. Learn more inside!

Chameleon and Reproducibility: LinnOS Case Study

This summer, a team of students worked on an experiment that ultimately became part of the LinnOS paper that infers the SSD performance with the help of its built in light neural network architecture. The LinnOS paper, which utilizes Chameleon testbed to provide a public executable workflow, will be presented in OSDI ’20 and is available here


Two of the students, Levent Toksoz and Mingzhe Hao, write about their experience in this Chameleon User Stories series. Toksoz is a recent graduate of the University of Chicago computer science masters program. He studied physics and math as an undergrad at the University of Michigan and is planning to apply to PhD programs in computer science. Hao is a Ph.D candidate of the UCARE group in the Department of Computer Science at the University of Chicago. His research interests include operating systems, storage systems, and distributed systems.

Packaging Experiments for Reproducibility

Chameleon integrates directly with Jupyter Notebook to provide an experimental environment that has everything you could need for research - a cloud testbed, a way to combine actionable code with written documentation, and sharing capabilities through Zenodo. Learn more about how to take advantage of all these capabilities and package your notebooks for publishing.