Applications and Evaluation of In-memory MapReduce

Abstract

In-memory storage techniques provide cloud applications with cheap, fast and large-scale RAM-based storage. By replicating data and providing adequate consistency control mechanisms, in-memory storage can simplify the design and implementation of highly scalable distributed applications. We argue that in-memory storage can increase the flexibility of the MapReduce parallel programming model without requiring additional communication facilities to propagate data updates. In this paper, we present several applications for our in-memory MapReduce framework from diverse problem domains including iterative and on-line data processing.