A Cloud Service Composition Optimization Based on HNN

doi:10.16182/j.issn1004731x.joss.19-FZ0341

Abstract

Abstract: With the rapid development of Cloud service application, how to effectively optimize the composition of Cloud services on cloud platform and improve the overall performance of cloud platform system have become an urgent research issue. In order to improve the efficiency of Cloud services, a combined optimization model based on Hopfield neural network is proposed. The problem of Cloud services is modeled. The problem is expressed as Hopfield Neural Network energy model for optimization, and a PSO group algorithm with Cauchy disturbance is designed to improve the Hopfield model. The experimental comparison shows that the method can improve the efficiency of Cloud service composition optimization more effectively than other typical algorithms.

Key words: Hopfield neural network, Composition optimization, Web service, Resource constraints, Load balancing, Cloud computing

CLC Number:

TP393
TP181

Zhang Huili, Li Zhihe. A Cloud Service Composition Optimization Based on HNN[J]. Journal of System Simulation, 2019, 31(11): 2335-2343.

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