MapReduce in the Clouds for Science

Abstract — The utility computing model introduced by cloud computing combined with the rich set of cloud infrastructure services offers a very viable alternative to traditional servers and computing clusters. MapReduce distributed data processing architecture has become the weapon of choice for data-intensive analyses in the clouds and in commodity clusters due to its excellent fault tolerance features, scalability and the ease of use. Currently, there are several options for using MapReduce in cloud environments, such as using MapReduce as a service, setting up one’s own MapReduce cluster on cloud instances, or using specialized cloud MapReduce runtimes that take advantage of cloud infrastructure services. In this paper, we introduce AzureMapReduce, a novel MapReduce runtime built using the Microsoft Azure cloud infrastructure services. AzureMapReduce architecture successfully leverages the high latency, eventually consistent, yet highly scalable Azure infrastructure services to provide an efficient, on demand alternative to traditional MapReduce clusters. Further we evaluate the use and performance of MapReduce frameworks, including AzureMapReduce, in cloud environments for scientific applications using sequence assembly and sequence alignment as use cases