Middleware alternatives for storm surge predictions in Windows Azure. Chandrasekar, K., Pathirage, M., Wijeratne, S., Mattocks, C., & Plale, B. In ScienceCloud '12 - 3rd Workshop on Scientific Cloud Computing, 2012. doi abstract bibtex Cloud computing is a resource of significant value to computational science, but has proven itself to be not immediately realizable by the researcher. The cloud providers that offer a Platform-as-a-Service (PaaS) platform should, in theory, offer a sound alternative to infrastructure-as-a- service as it could be easier to take advantage of for computational science kinds of problems. The objective of our study is to assess how well the Azure platform as a service can serve a particular class of computational science application. We conduct a performance evaluation using three approaches to executing a high-throughput storm surge application: using Sigiri, a large scale resource abstraction tool, Windows Azure HPC scheduler, and Daytona, an Iterative Map-reduce runtime for Azure. The differences in the approaches including early performance measures for up to 500 instances are discussed. Copyright © 2012 ACM.
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title = {Middleware alternatives for storm surge predictions in Windows Azure},
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abstract = {Cloud computing is a resource of significant value to computational science, but has proven itself to be not immediately realizable by the researcher. The cloud providers that offer a Platform-as-a-Service (PaaS) platform should, in theory, offer a sound alternative to infrastructure-as-a- service as it could be easier to take advantage of for computational science kinds of problems. The objective of our study is to assess how well the Azure platform as a service can serve a particular class of computational science application. We conduct a performance evaluation using three approaches to executing a high-throughput storm surge application: using Sigiri, a large scale resource abstraction tool, Windows Azure HPC scheduler, and Daytona, an Iterative Map-reduce runtime for Azure. The differences in the approaches including early performance measures for up to 500 instances are discussed. Copyright © 2012 ACM.},
bibtype = {inproceedings},
author = {Chandrasekar, K. and Pathirage, M. and Wijeratne, S. and Mattocks, C. and Plale, B.},
doi = {10.1145/2287036.2287040},
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