Dr. Xiaoyi Lu is a research assistant professor at The Ohio State University focusing on High Performance Interconnects and Protocols, Big Data Computing, Deep Learning, Parallel Computing, Virtualization, and Cloud Computing. In this blog post, we explore his research and usage of Chameleon Cloud.

November’s Chameleon User Experiments blog features Nanqinqin Li, a first-year PhD student at Princeton University. Learn more about Li, his summer research on reproducibility and Solid-State Drive Simulators, and learn where to replicate his experiment on Trovi!

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!

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.

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. 

The way you access the testbed will change -- for the bettter! You will be able to access the testbed via federated login allowing you to log in with your instritutional credentails or even your Google account -- read about the impact and schedule of this important change! 

We have created and shared a new Jupyter notebook that shows a better way to combine standard isolated Chameleon networks with DirectStitch capabilities. This more advanced method shows how to separate management of the stitched links from the compute nodes.

We are happy to announce that the Chameleon project has been extended for another 4 years! 

That’s four more years of working with a creative and talented user community that always wants to go someplace impossible – and takes us with them! 

The next four years will bring us integration with IoT, support for more innovative networking experiments, innovative new hardware, and even more support for reproducibility and experiment sharing. Read all about it in: https://www.cs.uchicago.edu/news/article/chameleon-phase-three/

We are SO looking forward to continuing to provide a platform for your research -- and learning about all the hot and cool things you do! 

Chameleon eliminates the need to involve campus IT staff and enables access to direct public cloud network connections to all Chameleon users.  It is now possible for any user to experiment with these advanced cloud networking technologies using Chameleon resources without the need for complicated campus networking configuration. Learn more about the capability in this blog. 

As with many projects and programming languages, there is more than one way to achieve a task when orchestrating Chameleon computing and network resources. As a result, experimenters may feel overwhelmed and choose to stick to the orchestration method they are familiar with even when another method might be more effective for the task in hand.