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Data locality has recently been extensively exploited in Cloud computing to improve system performance. However, when schedule Map tasks in Hadoop MapReduce framework working in a heterogeneous environment, existing methods either cannot reduce the occurrence of these Map tasks or injure fairness, thus degrading the system performance. In order to address this problem, this paper proposes a data locality...
Data Locality is one of the critical factors to affect performance. This paper proposes a next-k-node scheduling (NKS) method to improve the data locality of map tasks. The method first calculates the probabilities of each map task, and then preferentially schedules the one with the highest probability. It generates low probabilities for the tasks which satisfy node locality with the nodes to issue...
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...
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