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.
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.
This blog discusses a new experiment deployed on Chameleon called CIEF, a Cyber Infrastructure for Ecological Forecasting (Dietz & Matta, 2018). CIEF supports data-driven research in ecological forecasting to understand our ecosystem and drive policy. Examples include predicting environmental changes, corn production in the near to medium term, types of disease-carrying mosquitos, based on data related to air, land, and water.
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.
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.
Simpler SDN setups, a new Jupyter tutorial, and a new focus in the new year--more details inside!