An optimized MapReduce workflow scheduling algorithm for heterogeneous computing

Zhuo Tang, Min Liu, Almoalmi Ammar, Kenli Li, Keqin Li
<span title="2014-11-19">2014</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/qhbautqnvzgwvm3vvdvieylhwq" style="color: black;">Journal of Supercomputing</a> </i> &nbsp;
The MapReduce framework is considered to be an effective resolution for huge and parallel data processing. This paper treats a massive data processing workflow as a DAG graph consisting of MapReduce jobs. In a heterogeneous computing environment, the computation speed can be different even on the same slot depending on various jobs. For this problem, this paper proposes an optimized MapReduce workflow scheduling algorithm. This algorithm comprises a job prioritizing phase and a task assignment
more &raquo; ... hase. Firstly, the jobs can be classified as I/O-intensive and computing-intensive, and the priorities of all jobs are computed according to their corresponding types. Then, the suitable slots are allocated for each block, and the MapReduce tasks in the workflow are scheduled with respect to data locality. The experimental results show that the optimized MapReduce workflow scheduling algorithm can improve the performance of task scheduling and the rationality of resources allocation in heterogeneous computing.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11227-014-1335-2">doi:10.1007/s11227-014-1335-2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/cyayhon2fzgkbdvpb4edrigoae">fatcat:cyayhon2fzgkbdvpb4edrigoae</a> </span>
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