Middleware alternatives for storm surge predictions in Windows Azure

Authors:
Kavitha Chandrasekar;Milinda Pathirage;Saminda Wijeratne;Craig Mattocks;Beth Plale
Affiliations:
Indiana University, Bloomington, IN, USA;Indiana University, Bloomington, IN, USA;Indiana University, Bloomington, IN, USA;Miami University, Miami, FL, USA;Indiana University, Bloomington, IN, USA
Venue:
Proceedings of the 3rd workshop on Scientific Cloud Computing Date
Year:
2012

Citing 9
Cited 1

myExperiment: social networking for workflow-using e-scientists

Proceedings of the 2nd workshop on Workflows in support of large-scale science
On Building Scientific Workflow Systems for Data Management in the Cloud

ESCIENCE '08 Proceedings of the 2008 Fourth IEEE International Conference on eScience
The Trident Scientific Workflow Workbench

ESCIENCE '08 Proceedings of the 2008 Fourth IEEE International Conference on eScience
SciCumulus: A Lightweight Cloud Middleware to Explore Many Task Computing Paradigm in Scientific Workflows

CLOUD '10 Proceedings of the 2010 IEEE 3rd International Conference on Cloud Computing
AzureBlast: a case study of developing science applications on the cloud

Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
Cloud computing paradigms for pleasingly parallel biomedical applications

Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
Performing Large Science Experiments on Azure: Pitfalls and Solutions

CLOUDCOM '10 Proceedings of the 2010 IEEE Second International Conference on Cloud Computing Technology and Science
Trading Consistency for Scalability in Scientific Metadata

ESCIENCE '10 Proceedings of the 2010 IEEE Sixth International Conference on e-Science
Sigiri: uniform resource abstraction for grids and clouds

Concurrency and Computation: Practice & Experience

Storm surge simulation and load balancing in Azure cloud

Proceedings of the High Performance Computing Symposium

Quantified Score

Hi-index	0.00

Visualization

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.