MapReduce machine covering problem on a small number of machines

Yiwei Jiang (), Ping Zhou and Wei Zhou
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Yiwei Jiang: Zhejiang Gongshang University
Ping Zhou: Zhejiang Business College
Wei Zhou: Zhejiang Sci-Tech University

Journal of Combinatorial Optimization, 2019, vol. 38, issue 4, No 5, 1066-1076

Abstract: Abstract We study machine covering problem in MapReduce system. Each job consists of two sets of tasks, namely the map tasks and reduce tasks. A job’s reduce tasks can only be processed after all its map tasks are finished. The map tasks are fractional, i.e., they can be arbitrarily split and processed on different machines in parallel. Our goal is to maximize the minimum machine completion time. We consider two variants of the problem, namely the cases involving preemptive reduce tasks and non-preemptive reduce tasks. For preemptive reduce tasks, we present optimal solution algorithms for the problem on two and three machines. For non-preemptive reduce tasks, we provide an approximation algorithm with a tight worse-case ratio of $$\frac{4}{3}$$ 4 3 for the problem on two machines.

Keywords: MapReduce; Machine covering; Algorithm (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s10878-019-00436-8

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