An Autonomic Approach to Integrated HPC Grid and Cloud Usage

Clouds are rapidly joining high-performance Grids as viable computational platforms for scientific exploration and discovery, and it is clear that production computational infrastructures will integrate both these paradigms in the near future. As a result, understanding usage modes that are meaningful in such a hybrid infrastructure is critical. For example, there are interesting application workflows that can benefit from such hybrid usage modes to, per- haps, reduce times to solutions, reduce costs (in terms of currency or resource allocation), or handle unexpected runtime situations (e.g., unexpected delays in scheduling queues or unexpected failures). The primary goal of this paper is to experimentally investigate, from an applications perspective, how autonomics can enable interesting usage modes and scenarios for integrating HPC Grid and Clouds. Specifically, we used a reservoir characterization application workflow, based on Ensemble Kalman Filters (EnKF) for history matching, and the CometCloud autonomic Cloud engine on a hybrid platform consisting of the TeraGrid and Amazon EC2, to investigate 3 usage modes (or autonomic objectives) - acceleration, conservation and resilience.