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An adaptive approach to better load balancing in a consumer-centric cloud environment

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

Authors

Qi Liu

Weidong Cai

Jian Shen

Nigel Linge



Abstract

Pay-as-you-consume, as a new type of cloud computing paradigm, has become increasingly popular since a large number of cloud services are gradually opening up to consumers. It gives consumers a great convenience, where users no longer need to buy their hardware resources, but are confronted with how to deal effectively with data from the cloud. How to improve the performance of the cloud platform as a consumer-centric cloud computing model becomes a critical issue. Existing heterogeneous distributed computing systems provide efficient parallel and high fault tolerant and reliable services, due to its characteristics of managing largescale clusters. Though the latest cloud computing cluster meets the need for faster job execution, more effective use of computing resources is still a challenge. Presently proposed methods concentrated on improving the execution time of incoming jobs, e.g., shortening the MapReduce (MR) time. In this paper, an adaptive scheme is offered to achieve time and space efficiency in a heterogeneous cloud environment. A dynamic speculative execution strategy on real-time management of cluster resources is presented to optimize the execution time of Map phase, and a prediction model is used for fast prediction of task execution time. Combing the prediction model with a multi-objective optimization algorithm, an adaptive solution to optimize the performance of space-time is obtained. Experimental results depict that the proposed scheme can allocate tasks evenly and improve work efficiency in a heterogeneous cluster.

Journal Article Type Article
Acceptance Date Sep 1, 2016
Online Publication Date Oct 26, 2016
Publication Date 2016-08
Deposit Date Jan 30, 2017
Publicly Available Date Mar 16, 2017
Journal IEEE Transactions on Consumer Electronics
Print ISSN 0098-3063
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 62
Issue 3
Pages 243-250
DOI https://doi.org/10.1109/tce.2016.7613190
Keywords K-ELM, Pay-as-you-consume, MapReduce, Load balancing, Prediction model
Public URL http://researchrepository.napier.ac.uk/Output/451203
Contract Date Mar 16, 2017

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