Executing Storm Surge Ensembles on PAAS Cloud

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


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.


Execution Time Virtual Machine Storm Surge Work Role Cloud Environment 
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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.


  1. 1.
    National Hurricane Center.
  2. 2.
  3. 3.
    Chakraborty, A., Pathirage, M., Suriarachchi, I., Chandrasekar, K., Mattocks, C., Plale, B.: Storm surge simulation and load balancing in azure cloud. In: Proc. 21st High Performance Computing Symposium (HPC’13), HPC’13, pp. 1–9. SCS and ACM (2013)Google Scholar
  4. 4.
    Chandrasekar, K., Pathirage, M., Wijeratne, S., Mattocks, C., Plale, B.: Middleware alternatives for storm surge predictions in windows azure. In: Proc. 3rd workshop on Scientific Cloud Computing Date, ScienceCloud ‘12, pp. 3–12 (2012)Google Scholar
  5. 5.
    Glahn, B., Taylor, A., Kurkowski, N., Shaffer, W.: The role of the slosh model in national weather service storm surge forecasting. National Weather Digest 33(1), 3–14 (2009)Google Scholar
  6. 6.
    Gunarathne, T., Wu, T.L., Qiu, J., Fox, G.: Cloud computing paradigms for pleasingly parallel biomedical applications. In: Proc. 19th ACM Int. Symposium on High Performance Distributed Computing, HPDC ‘10, pp. 460–469. ACM, New York, NY, USA (2010)Google Scholar
  7. 7.
    Gunarathnea, T., Qiu, J., Fox, G.: Iterative mapreduce for azure cloud. In: Proc. Cloud Computing and Its Applications, CCA, CCA ‘11 (2011)Google Scholar
  8. 8.
    Lee, C.A.: A perspective on scientific cloud computing. In: Proc. 19th ACM Int. Symposium on High Performance Distributed Computing, HPDC ‘10, pp. 451–459 (2010)Google Scholar
  9. 9.
    Li, J., Humphrey, M., Agarwal, D.A., Jackson, K.R., van Ingen, C., Ryu, Y.: eScience in the cloud: A modis satellite data reprojection and reduction pipeline in the windows azure platform. In: IPDPS, pp. 1–10 (2010)Google Scholar
  10. 10.
    Lu, W., Jackson, J., Barga, R.: AzureBlast: a case study of developing science applications on the cloud. In: Proc. 19th ACM Int. Symposium on High Performance Distributed Computing, HPDC ‘10, pp. 413–420 (2010)Google Scholar
  11. 11.
    Lu, W., Jackson, J., Ekanayake, J., Barga, R.S., Araujo, N.: Performing large science experiments on azure: Pitfalls and solutions. In: CloudCom, pp. 209–217 (2010)Google Scholar
  12. 12.
    Mehrotra, P., Djomehri, J., Heistand, S., Hood, R., Jin, H., Lazanoff, A., Saini, S., Biswas, R.: Performance evaluation of amazon EC2 for NASA HPC applications. In: Proceedings of the 3rd workshop on Scientific Cloud Computing Date, pp. 41–50. New York, NY, USA (2012)Google Scholar
  13. 13.
    Moretti, C., Thrasher, A., Yu, L., Olson, M., Emrich, S.J., Thain, D.: A framework for scalable genome assembly on clusters, clouds, and grids. IEEE Trans. Parallel Distributed Systems 23(12), 2189–2197 (2012)CrossRefGoogle Scholar
  14. 14.
    de Oliveira, D., Ogasawara, E., Baião, F., Mattoso, M.: Scicumulus: A lightweight cloud middleware to explore many task computing paradigm in scientific workflows. In: Proc. IEEE 3rd Int. Conf. on Cloud Computing, CLOUD ‘10, pp. 378–385. IEEE Computer Society, Washington, DC, USA (2010)Google Scholar
  15. 15.
    Pandey, S., Karunamoorthy, D., Buyya, R.: Workflow engine for clouds. In: Cloud Computing, Principles and Paradigms, Wiley Series on Parallel and Distributed Computing, pp. 321–344 (2011)Google Scholar
  16. 16.
    Simpson, R., Saffir, H.: Tropical cyclone destructive potential by integrated kinetic energy. Bull. Amer. Meteor. Soc. 88, 1799–1800 (2007)CrossRefGoogle Scholar
  17. 17.
    Withana, E.C., Plale, B.: Sigiri: uniform resource abstraction for grids and clouds. Concurrency and Computation: Practice and Experience 24(18), 1532–0626 (2012)CrossRefGoogle Scholar

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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