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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References
National Hurricane Center. http://www.nhc.noaa.gov/surge/faq.php#4x
NOAA SLOSH display program. http://slosh.nws.noaa.gov/sloshPub/#sloshDsp
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)
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)
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)
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)
Gunarathnea, T., Qiu, J., Fox, G.: Iterative mapreduce for azure cloud. In: Proc. Cloud Computing and Its Applications, CCA, CCA ‘11 (2011)
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)
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)
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)
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)
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)
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)
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)
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)
Simpson, R., Saffir, H.: Tropical cyclone destructive potential by integrated kinetic energy. Bull. Amer. Meteor. Soc. 88, 1799–1800 (2007)
Withana, E.C., Plale, B.: Sigiri: uniform resource abstraction for grids and clouds. Concurrency and Computation: Practice and Experience 24(18), 1532–0626 (2012)
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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