Storm surge simulation and load balancing in Azure cloud

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.