Category – User Contributions

A Scalable Cyberinfrastructure for Repeatable Ecological Research

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.

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.

Maximizing Performance in Distributed Systems with a Power Budget

One challenge for power budgeting systems is how to power cap dependent applications for high performance. Existing approaches, however, have major limitations. Our work proposes a hierarchical, distributed, dynamic power management system for dependent applications.

Transferring Large Data Flows on Chameleon

Ready-to-use Data Transfer Node (DTN) is provided, and it can be used to provide efficient network data transfer over a long fat network. In addition, a Chameleon Complex Appliance is publish for easy spawning a set of DTNs in Chameleon Cloud.

Popper: A DevOps Approach to Carrying Out Experiments on Chameleon

Popper is a protocol for creating reproducible experiments designed to leverage popular DevOps tools and techniques, such as Git, Docker and continuous integration (CI) in order to produce experiments that can be re-executed on different environments with a single command.

ENOS: a Framework for Experimenting with OpenStack

ENOS is an integrated framework that facilitates experimenting with OpenStack. ENOS allows researchers to easily express different configurations, enabling fine-grained investigations of OpenStack services. ENOS collects performance metrics at runtime and stores them for post-mortem analysis and sharing.