A configurable experimental environment for large-scale edge to cloud research

Recent news

  • Chameleon Tickets of the Year 2024

    Chameleon Support: Top Issue Areas and How to Resolve Them
    by Marc Richardson

    Facing network issues, floating IP shortages, or login troubles on Chameleon? This blog post highlights the top challenges users encountered over the past year and shares practical tips and tricks to resolve them quickly. Learn how to tackle firewall misconfigurations, manage floating IPs efficiently, troubleshoot login errors, and more. Save time and streamline your Chameleon experience!

  • Happy Holidays 2024 from the Chameleon Team!

    Updates on Chameleon during the winter break
    by Kate Keahey

    Season's Greetings from the Chameleon Team!

    As the year draws to a close we want to thank you for another fabulous year of innovative ideas and exciting resaerch. May your holidays be magical and joyful, and the New Year full of exciting adventures! 

    As in the years before, the Chameleon Help Desk will be on snooze (make sure to get your tickets in by the 20th!) but we will make up for it by continuing our Chameleon for Christmas "double lease" tradition -- between 19th and 22nd (inclusive) you can make 14 day leases -- and this does include GPUs! 

    Looking forward to working with you again in the New Year! 

  • Chameleon Changelog for November 2024

    by Mark Powers

    This month we have presentations from the 5th Chameleon User Meeting on practical reproducibility, and lots of housekeeping on Chameleon. These include improvements to IP availability, and fixes to the core testbed, Trovi, and CHI@Edge.

  • Empowering the Edge: Breaking Heterogeneity Barriers in Cloud-based ML Training

    Optimizing Federated Learning for Heterogeneous Edge Devices
    by Redwan Ibne Seraj Khan

    Learn how researcher Redwan Khan uses Chameleon to develop FedCaSe, an innovative framework that tackles the challenges of distributed machine learning across diverse edge devices. This groundbreaking research demonstrates up to 29x improvement in client participation and 81x better data access efficiency, paving the way for more accessible and efficient AI systems.

  • Building MPI Clusters on Chameleon: A Practical Guide

    Simplifying Distributed Computing Setup with Jupyter, Ansible, and Python
    by Michael Sherman

    Running distributed applications across multiple nodes is a common need in scientific computing, but setting up MPI clusters can be challenging, especially in cloud environments. In this post, we'll explore a template for creating MPI clusters on Chameleon that handles the key configuration steps automatically, letting you focus on your research rather than infrastructure setup.