Improving Data Locality of MapReduce by Scheduling in Homogeneous Computing Environments

@article{Zhang2011ImprovingDL,
  title={Improving Data Locality of MapReduce by Scheduling in Homogeneous Computing Environments},
  author={Xiaohong Zhang and Zhiyong Zhong and Shengzhong Feng and Bibo Tu and Jianping Fan},
  journal={2011 IEEE Ninth International Symposium on Parallel and Distributed Processing with Applications},
  year={2011},
  pages={120-126}
}
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 requests, so it can reserve these tasks to these nodes. We have implemented the NKS method in… CONTINUE READING
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Adaptive task scheduling for multijob mapreduce environments

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