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Optimizing Both the User Requirements and the Load Balancing in the Volunteer Computing System by using Markov Chain Model

Optimizing Both the User Requirements and the Load Balancing in the Volunteer Computing System by using Markov Chain Model

Abdeldjalil Ledmi, Hakim Bendjenna, Hemam Sofiane Mounine
Copyright: © 2018 |Volume: 14 |Issue: 1 |Pages: 28
ISSN: 1548-1115|EISSN: 1548-1123|EISBN13: 9781522542681|DOI: 10.4018/IJEIS.2018010103
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MLA

Ledmi, Abdeldjalil, et al. "Optimizing Both the User Requirements and the Load Balancing in the Volunteer Computing System by using Markov Chain Model." IJEIS vol.14, no.1 2018: pp.35-62. http://doi.org/10.4018/IJEIS.2018010103

APA

Ledmi, A., Bendjenna, H., & Mounine, H. S. (2018). Optimizing Both the User Requirements and the Load Balancing in the Volunteer Computing System by using Markov Chain Model. International Journal of Enterprise Information Systems (IJEIS), 14(1), 35-62. http://doi.org/10.4018/IJEIS.2018010103

Chicago

Ledmi, Abdeldjalil, Hakim Bendjenna, and Hemam Sofiane Mounine. "Optimizing Both the User Requirements and the Load Balancing in the Volunteer Computing System by using Markov Chain Model," International Journal of Enterprise Information Systems (IJEIS) 14, no.1: 35-62. http://doi.org/10.4018/IJEIS.2018010103

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Abstract

This article describes how in volunteer cloud computing systems, some resources are volunteered by the hosts. These systems became more powerful and attractive because they provide a highest power computing. However, to satisfy the user requirements and the system performance in this kind of the system is a crucial challenge. In this article, the authors propose a new architecture for the volunteer cloud computing systems to allow balancing the load between volunteer clouds in a decentralized manner, and between resources inside a volunteer cloud in centralized manner. Moreover, their proposal shows more advantages: First, selecting a resource according to the user requirements and to the system performance. Second, estimating the volunteer resource failure probability by using the stochastic process Markov chain model. Experimental results using the PeerSim Simulator is established to verify the efficacy of the proposed system and promising results are obtained.

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