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Mathematical Problems in Engineering
Volume 2016, Article ID 9529526, 14 pages
http://dx.doi.org/10.1155/2016/9529526
Research Article

Multiobjective Reliable Cloud Storage with Its Particle Swarm Optimization Algorithm

1Institute of Software Engineering, School of Software, Xidian University, Xi’an 710071, China
2School of Computer Science and Technology, Xidian University, Xi’an, China

Received 26 May 2016; Accepted 9 November 2016

Academic Editor: Marco Mussetta

Copyright © 2016 Xiyang Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Information abounds in all fields of the real life, which is often recorded as digital data in computer systems and treated as a kind of increasingly important resource. Its increasing volume growth causes great difficulties in both storage and analysis. The massive data storage in cloud environments has significant impacts on the quality of service (QoS) of the systems, which is becoming an increasingly challenging problem. In this paper, we propose a multiobjective optimization model for the reliable data storage in clouds through considering both cost and reliability of the storage service simultaneously. In the proposed model, the total cost is analyzed to be composed of storage space occupation cost, data migration cost, and communication cost. According to the analysis of the storage process, the transmission reliability, equipment stability, and software reliability are taken into account in the storage reliability evaluation. To solve the proposed multiobjective model, a Constrained Multiobjective Particle Swarm Optimization (CMPSO) algorithm is designed. At last, experiments are designed to validate the proposed model and its solution PSO algorithm. In the experiments, the proposed model is tested in cooperation with 3 storage strategies. Experimental results show that the proposed model is positive and effective. The experimental results also demonstrate that the proposed model can perform much better in alliance with proper file splitting methods.