Online MapReduce processing on two identical parallel machines

Jidan Huang (), Feifeng Zheng (), Yinfeng Xu () and Ming Liu ()
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Jidan Huang: Donghua University
Feifeng Zheng: Donghua University
Yinfeng Xu: Donghua University
Ming Liu: Tongji University

Journal of Combinatorial Optimization, 2018, vol. 35, issue 1, No 17, 216-223

Abstract: Abstract In this work we investigate the online over-list MapReduce processing problem on two identical parallel machines, aiming at minimizing the makespan. Jobs are revealed one by one, and each job consists of one map task and one reduce task. The map task can be arbitrarily split and processed on both machines simultaneously, while the reduce task has to be processed on a single machine and it cannot be started unless the map task has been completed. We first show that the general case of the problem reduces to the classical two machine online scheduling model with an optimal competitive ratio of 3/2. For a special case where the map task is at least as long as the reduce task, we prove that no online algorithm can be less than 4/3-competitive. An optimal Greedy algorithm with a matching competitive ratio is proposed as well.

Keywords: MapReduce; Online scheduling; Competitive ratio (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s10878-017-0167-4

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