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Algorithms 2019, 12(1), 5;

MAPSkew: Metaheuristic Approaches for Partitioning Skew in MapReduce

Departamento de Pós Graduação em Ciências da Computação (MDCC), Universidade Federal do Ceará, Fortaleza, CE 60020-181, Brazil
Centro de Ciências e Tecnologia (CCT), Universidade de Fortaleza (UNIFOR), Fortaleza, CE 60020-181, Brazil
Author to whom correspondence should be addressed.
Received: 19 October 2018 / Revised: 23 November 2018 / Accepted: 10 December 2018 / Published: 24 December 2018
(This article belongs to the Special Issue MapReduce for Big Data)
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MapReduce is a parallel computing model in which a large dataset is split into smaller parts and executed on multiple machines. Due to its simplicity, MapReduce has been widely used in various applications domains. MapReduce can significantly reduce the processing time of a large amount of data by dividing the dataset into smaller parts and processing them in parallel in multiple machines. However, when data are not uniformly distributed, we have the so called partitioning skew, where the allocation of tasks to machines becomes unbalanced, either by the distribution function splitting the dataset unevenly or because a part of the data is more complex and requires greater computational effort. To solve this problem, we propose an approach based on metaheuristics. For evaluating purposes, three metaheuristics were implemented: Simulated Annealing, Local Beam Search and Stochastic Beam Search. Our experimental evaluation, using a MapReduce implementation of the Bron-Kerbosch Clique Algorithm, shows that the proposed method can find good partitionings while better balancing data among machines. View Full-Text
Keywords: MapReduce; partitioning skew; metaheuristics; parallel computing; high performance computing MapReduce; partitioning skew; metaheuristics; parallel computing; high performance computing

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Pericini, M.H.M.; Leite, L.G.M.; De Carvalho-Junior, F.H.; Machado, J.C.; Rezende, C.A. MAPSkew: Metaheuristic Approaches for Partitioning Skew in MapReduce. Algorithms 2019, 12, 5.

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