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Executing Storm Surge Ensembles on PAAS Cloud

  • Abhirup ChakrabortyEmail author
  • Milinda Pathirage
  • Isuru Suriarachchi
  • Kavitha Chandrasekar
  • Craig Mattocks
  • Beth Plale
Chapter

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.

Keywords

Execution Time Virtual Machine Storm Surge Work Role Cloud Environment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

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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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Abhirup Chakraborty
    • 1
    Email author
  • Milinda Pathirage
    • 1
  • Isuru Suriarachchi
    • 1
  • Kavitha Chandrasekar
    • 1
  • Craig Mattocks
    • 2
  • Beth Plale
    • 1
  1. 1.School of Informatics and ComputingIndiana UniversityBloomingtonUSA
  2. 2.Rosenstiel School of Marine and Atmospheric ScienceMiami UniversityMiamiUSA

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