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A speculative approach to spatial-temporal efficiency with multi-objective optimization in a heterogeneous cloud environment

Liu, Qi; Cai, Weidong; Shen, Jian; Fu, Zhangjie; Liu, Xiaodong; Linge, Nigel

Authors

Qi Liu

Weidong Cai

Jian Shen

Zhangjie Fu

Nigel Linge



Abstract

A heterogeneous cloud system, for example, a Hadoop 2.6.0 platform, provides distributed but cohesive services with rich features on large-scale management, reliability, and error tolerance. As big data processing is concerned, newly built cloud clusters meet the challenges of performance optimization focusing on faster task execution and more efficient usage of computing resources. Presently proposed approaches concentrate on temporal improvement, that is, shortening MapReduce time, but seldom focus on storage occupation; however, unbalanced cloud storage strategies could exhaust those nodes with heavy MapReduce cycles and further challenge the security and stability of the entire cluster. In this paper, an adaptive method is presented aiming at spatial–temporal efficiency in a heterogeneous cloud environment. A prediction model based on an optimized Kernel-based Extreme Learning Machine algorithm is proposed for faster forecast of job execution duration and space occupation, which consequently facilitates the process of task scheduling through a multi-objective algorithm called time and space optimized NSGA-II (TS-NSGA-II). Experiment results have shown that compared with the original load-balancing scheme, our approach can save approximate 47–55 s averagely on each task execution. Simultaneously, 1.254‰ of differences on hard disk occupation were made among all scheduled reducers, which achieves 26.6% improvement over the original scheme.

Journal Article Type Article
Acceptance Date Jun 12, 2016
Online Publication Date Aug 14, 2016
Publication Date Nov 21, 2016
Deposit Date Jan 30, 2017
Publicly Available Date Nov 14, 2017
Journal Security and Communication Networks
Print ISSN 1939-0114
Publisher Wiley
Peer Reviewed Peer Reviewed
Volume 9
Issue 17
Pages 4002-4012
DOI https://doi.org/10.1002/sec.1582
Keywords MapReduce; cloud storage; load balancing; multi-objective optimization; prediction model
Public URL http://researchrepository.napier.ac.uk/Output/451187
Contract Date Nov 14, 2017

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