Algorithms for the semi-online MapReduce scheduling problem on two identical machines

Article type: Research Article

Authors: Zheng, Quanchang^a

Affiliations: School of Management Science, Qufu Normal University, Rizhao, China

Correspondence: [*] Corresponding author. Quanchang Zheng, School of Management Science, Qufu Normal University, Rizhao 276800, China. E-mail: [email protected].

Abstract: We investigate the semi-online problem of MapReduce scheduling on two parallel machines. We aim to minimize the makespan. Jobs are released over-list, and each job includes a map task and a reduce task. The job’s map task can be preemptive and scheduled simultaneously onto different machines, however, the reduce task is non-preemptive. The job’s reduce task needs to wait for its map task to complete before starting. We consider the following two versions: Firstly, we know the processing time of the largest reduce task beforehand, and then design a 4/3-competitive optimal semi-online algorithm. Secondly, we know in advance the value of the reduce task with the largest processing time and the the total sum of the processing times. Then we present a 4/3-competitive semi-online algorithm. We conclude that the algorithm is the best possible when the largest reduce task meets certain conditions.

Keywords: MapReduce system, semi-online, scheduling, competitive ratio, makespan

DOI: 10.3233/JIFS-239276

Journal: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024