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 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. 

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.

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. 

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. 

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.

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. 

Jupyter notebooks are a great tool for structuring your computer science experiments on Chameleon because they allow you to iterate on your idea interactively, intuitively, and quickly. But, it may not be obvious how you can leverage this tool for running an experiment...

Did you ever want to create a lease for a specific node? Did you ever want to create a lease that does NOT include a specific node? Ignore a node that has been reserved? Reserve a whole rack perhaps? Or just a few nodes but on the same rack? Then look no farther; here are five tips and tricks for node selection and node avoidance!