Chances are, if you're using Chameleon today, you're probably utilizing either the GUI or the CLI (or a mixture of both.) Did you know there's a Python library that makes it easy to script your Chameleon experiments? In January we announced the public release of python-chi, the official Python library for CHI (Chameleon Infrastructure), which is exactly that. Read on to learn all about python-chi and how to easily use it to experiment on Chameleon.
Sit down with Fei Yeh, an associate researcher with the International Center for Advanced Internet Research (iCAIR) at Northwestern University, to learn how a Chameleon associate site was set up, how you can use it, and his experience.
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.
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 ...
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.