Abstract
Cloud computing services are becoming increasingly viable for scientific model execution. As a leased computational resource, cloud computing enables a computational modeler at a smaller university to carry out sporadic large-scale experiments, and allows others to pay for CPU cycles as needed, without incurring high maintenance costs of a large compute system. In this chapter, we discuss the issues involved in running high throughput ensemble applications on a Platform-as-a-Service cloud. We compare two frameworks deploying and running these applications, namely Sigiri and MapReduce. We motivate the need for a pipelined architecture to application deployment, and discus a couple of methodologies to balance the loads, minimize storage overhead, and reduce overall execution time.
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Acknowledgements
This work is funded by the National Science Foundation under grant OCI 1148359. We are grateful to Microsoft for sponsored access to Azure compute resources.
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Chakraborty, A., Pathirage, M., Suriarachchi, I., Chandrasekar, K., Mattocks, C., Plale, B. (2014). Executing Storm Surge Ensembles on PAAS Cloud. In: Li, X., Qiu, J. (eds) Cloud Computing for Data-Intensive Applications. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-1905-5_11
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DOI: https://doi.org/10.1007/978-1-4939-1905-5_11
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