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
A perspective on scientific cloud computing
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
Middleware alternatives for storm surge predictions in Windows Azure
Proceedings of the 3rd workshop on Scientific Cloud Computing Date
Performance evaluation of Amazon EC2 for NASA HPC applications
Proceedings of the 3rd workshop on Scientific Cloud Computing Date
Sigiri: uniform resource abstraction for grids and clouds
Concurrency and Computation: Practice & Experience
A Framework for Scalable Genome Assembly on Clusters, Clouds, and Grids
IEEE Transactions on Parallel and Distributed Systems
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Cloud computing platforms are drawing increasing attention of the scientific research communities. By providing a framework to lease computation resources, cloud computing enables the scientists to carry out large-scale experiments in a cost-effective fashion without incurring high setup and maintenance costs of a large compute system. In this paper, we study the implementation and scalability issues in deploying a particular class of computational science applications. Using Platform-as-a-Service (PAAS) of Windows Azure cloud, we implement a high-throughput Storm-Surge Simulation in both a middleware framework for deploying jobs (in cloud and grid environment) and a MapReduce framework---a data parallel programming model for processing large data sets. We present the detailed techniques to balance the simulation loads while parallelizing the application across a large number of nodes.