An adaptive parallel execution strategy for cloud-based scientific workflows
Concurrency and Computation: Practice & Experience
A Provenance-based Adaptive Scheduling Heuristic for Parallel Scientific Workflows in Clouds
Journal of Grid Computing
More for your money: exploiting performance heterogeneity in public clouds
Proceedings of the Third ACM Symposium on Cloud Computing
Evaluating cloud storage services for tightly-coupled applications
Euro-Par'12 Proceedings of the 18th international conference on Parallel processing workshops
A comparative study of high-performance computing on the cloud
Proceedings of the 22nd international symposium on High-performance parallel and distributed computing
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Clouds enable novel execution modes often supported by advanced capabilities such as autonomic schedulers. These capabilities are predicated upon an accurate estimation and calculation of runtimes on a given infrastructure. Using a well understood high-performance computing workload, we find strong fluctuations from the mean performance on EC2 and Eucalyptus-based cloud systems. Our analysis eliminates variations in IO and computational times as possible causes, we find that variations in communication times account for the bulk of the experiment-to-experiment fluctuations of the performance.