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A Survey of Speculative Execution Strategy in MapReduce

Liu, Qi; Jin, Dandan; Liu, Xiaodong; Linge, Nigel


Qi Liu

Dandan Jin

Nigel Linge


X Sun

A Liu

H C Chao

E Bertino


MapReduce is a parallel computing programming model designed to process large-scale data. Therefore, the accuracy and efficiency for computing are needed to be assured and speculative execution is an efficient method for calculation of fault tolerance. It reaches the goals of shortening the execution time and increasing the cluster throughput through selecting slow tasks and speculative copy these tasks on a fast machine to be executed. Hadoop naïve speculative execution strategy assumes that the cluster is homogeneous, and this assumption leads to the poor performance in heterogeneous environment. Several speculative execution strategies which aim to improve the MapReduce Performance in the heterogeneous environments are reviewed in this paper like LATE, MCP, ex-MCP and ERUL, then the comparison between these methods are listed.

Presentation Conference Type Conference Paper (Published)
Conference Name the 2nd International Conference on Cloud Computing and Security; Lecture Notes in Computer Science
Start Date Jul 29, 2016
End Date Jul 31, 2016
Acceptance Date May 1, 2016
Online Publication Date Nov 1, 2016
Publication Date 2016
Deposit Date Jan 30, 2017
Electronic ISSN 1611-3349
Publisher Springer
Pages 296-307
Series Title Lecture Notes in Computer Science
Series ISSN 0302-9743
Book Title Cloud Computing and Security
ISBN 978-3-319-48670-3; 978-3-319-48671-0
Keywords Hadoop, Map Reduce, Speculative execution, Heterogeneous environment
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