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Journal of Applied Mathematics
Volume 2013, Article ID 407267, 12 pages
Research Article

A Fast Optimization Method for Reliability and Performance of Cloud Services Composition Application

1School of Mathematics and Computer Science, Hubei University of Arts and Science, Xiangyang 441053, China
2School of Computer Science, Colorado Technical University, Colorado Springs, CO 80907, USA

Received 15 April 2013; Accepted 13 September 2013

Academic Editor: Rung Ching Chen

Copyright © 2013 Zhao Wu 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.


At present the cloud computing is one of the newest trends of distributed computation, which is propelling another important revolution of software industry. The cloud services composition is one of the key techniques in software development. The optimization for reliability and performance of cloud services composition application, which is a typical stochastic optimization problem, is confronted with severe challenges due to its randomness and long transaction, as well as the characteristics of the cloud computing resources such as openness and dynamic. The traditional reliability and performance optimization techniques, for example, Markov model and state space analysis and so forth, have some defects such as being too time consuming and easy to cause state space explosion and unsatisfied the assumptions of component execution independence. To overcome these defects, we propose a fast optimization method for reliability and performance of cloud services composition application based on universal generating function and genetic algorithm in this paper. At first, a reliability and performance model for cloud service composition application based on the multiple state system theory is presented. Then the reliability and performance definition based on universal generating function is proposed. Based on this, a fast reliability and performance optimization algorithm is presented. In the end, the illustrative examples are given.