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.

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.

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.

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 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 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.