@inproceedings{
 title = {Middleware alternatives for storm surge predictions in Windows Azure},
 type = {inproceedings},
 year = {2012},
 id = {bca26de9-698f-398f-8612-6fcde4ac8aeb},
 created = {2019-10-01T17:20:47.469Z},
 file_attached = {false},
 profile_id = {42d295c0-0737-38d6-8b43-508cab6ea85d},
 last_modified = {2019-10-01T17:23:40.528Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Chandrasekar2012},
 folder_uuids = {73f994b4-a3be-4035-a6dd-3802077ce863},
 private_publication = {false},
 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},
 booktitle = {ScienceCloud '12 - 3rd Workshop on Scientific Cloud Computing}
}