Chameleon use for COVID-19 projects

Most of you have probably seen the NSF Dear Colleague Letter regarding resource contributions of NSF-funded infrastructure to the novel coronavirus (COVID-19) research. We have obtained permission from NSF to join this effort and extend the range of Chameleon supported projects to include projects working on how to model and understand the spread of COVID-19 for a limited time. 

All About Traces

The workload traces from data centers facilitate research on the design of computer systems, job scheduling, and resource management.  Researchers can analyze the traces and replicate real-life workloads for their experiments. In this blog, we will briefly review some major released traces and introduce the benefits of using a Chameleon-developed trace generator for easily creating traces from cloud providers who use OpenStack. 

Chameleon Changelog for February 2020

Introducing a new networking capability: connect your Chameleon networks directly to AWS networks via DirectConnect! And, we discuss the addition of 40 new GPU cards at CHI@UC.

Accessing multiple nodes in a private network without DNS using Jupyter Notebook

A Jupyter notebook has been added to your Chameleon Jupyter Hub environment to show how to automate deploying a server and several clients which are configured with the names and IPs for every single other host in a custom isolated network. Also included are examples of several tricks you might find useful for deploying a complex experiment.

Chameleon Changelog for December 2019

From everyone at Chameleon, we hope you had a pleasant holiday and welcome to the new year! Details inside about new HTTPS capabilities and important webinar/conference dates to kick things off in 2020.

The “History Command” of Chameleon

The history command available in Bash is a useful tool, and you probably use it frequently in your daily routine jobs to check the history of the commands executed by the user. In this blog, we will see how an equivalent tool in Chameleon can help you check the experiment setup events you performed on Chameleon. 

Dynamo and Chameleon Aid Weather Scientists

Modern computational science depends on many complex, compute, and data-intensive applications operating on distributed datasets that originate from a variety of scientific instruments and data repositories. Two major challenges for these applications are: (1) the provisioning of compute resources and (2) the integration of data into the scientists’ workflow.

Chameleon Changelog for October 2019

There is a poem about the 5th of November, but sadly I couldn't find a way to adapt it for this changelog. Anyways, learn about our new KVM cloud! And we also packed in a few other goodies this month.