Accelerating MapReduce with Distributed Memory Cache

MapReduce is a partition-based parallel programming model and framework enabling easy development of scalable parallel programs on clusters of commodity machines. In order to make time-intensive applications benefit from MapReduce on small scale clusters, this paper proposes a new method to improve the performance of MapReduce by using distributed memory cache as a high speed access between map tasks and reduce tasks. Map outputs sent to the distributed memory cache can be gotten by reduce tasks as soon as possible. Experiment results show that our prototype´s performance is much better than that of the original on small scale clusters. To our knowledge, this is the first effort to accelerate MapReduce with the help of distributed memory cache.