A Survey of Speculative Execution Strategy in MapReduce

Research Output

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.

Date:

01 November 2016
Publication Status:

Published
Publisher

Springer Nature
DOI:

10.1007/978-3-319-48671-0_27
Library of Congress:

QA75 Electronic computers. Computer science
Dewey Decimal Classification:

004 Data processing & computer science

http://researchrepository.napier.ac.uk/output/451268 <p>Liu, Q., Jin, D., Liu, X., & Linge, N. (2016). A Survey of Speculative Execution Strategy in MapReduce. In A. Liu, H. C. Chao, E. Bertino, & X. Sun (Eds.), <i>Cloud Computing and Security</i>. , (296-307). https://doi.org/10.1007/978-3-319-48671-0_27</p>

Citation

Liu, Q., Jin, D., Liu, X., & Linge, N. (2016). A Survey of Speculative Execution Strategy in MapReduce. In A. Liu, H. C. Chao, E. Bertino, & X. Sun (Eds.), Cloud Computing and Security. , (296-307). https://doi.org/10.1007/978-3-319-48671-0_27