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A Hybrid Meta-Heuristic Approach for QoS-Aware Cloud Service Composition

A Hybrid Meta-Heuristic Approach for QoS-Aware Cloud Service Composition

S. Bharath Bhushan, Pradeep C. H. Reddy
Copyright: © 2018 |Volume: 15 |Issue: 2 |Pages: 20
ISSN: 1545-7362|EISSN: 1546-5004|EISBN13: 9781522542452|DOI: 10.4018/IJWSR.2018040101
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MLA

Bhushan, S. Bharath, and Pradeep C. H. Reddy. "A Hybrid Meta-Heuristic Approach for QoS-Aware Cloud Service Composition." IJWSR vol.15, no.2 2018: pp.1-20. http://doi.org/10.4018/IJWSR.2018040101

APA

Bhushan, S. B. & Reddy, P. C. (2018). A Hybrid Meta-Heuristic Approach for QoS-Aware Cloud Service Composition. International Journal of Web Services Research (IJWSR), 15(2), 1-20. http://doi.org/10.4018/IJWSR.2018040101

Chicago

Bhushan, S. Bharath, and Pradeep C. H. Reddy. "A Hybrid Meta-Heuristic Approach for QoS-Aware Cloud Service Composition," International Journal of Web Services Research (IJWSR) 15, no.2: 1-20. http://doi.org/10.4018/IJWSR.2018040101

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Abstract

Cloud is evolving as an outstanding platform to deliver cloud services on a pay-as-you-go basis. The selection and composition of cloud services based on QoS criteria is formulated as NP hard optimization problem. Traditionally, many optimization techniques are applied to solve it, but it suffers from slow convergence speed, large number of calculations, and falling into local optimum. This article proposes a hybrid particle swarm optimization (HPSO) technique that combines particle swarm optimization (PSO) and fruit fly (FOA) to perform the evolutionary search process. The following determines a pareto optimal service set which is non-dominated solution set as input to the proposed HPSO. In the proposed HPSO, the parameters such as position and velocity are redefined, and while updating, the smell operator of fruit fly is used to overcome the prematurity of PSO. The FOA enhances the convergence speed with good fitness value. The experimental results show that the proposed HPSO outperforms the simple particle swarm optimization in terms of fitness value, execution time, and error rate.

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