A Cloud Service Composition Optimization Based on HNN

Authors

Huili Zhang, 1. Linfen Vocational and Technical College, Linfen 041000, China; ;2. School of Educational Science, Shanxi Normal University, Linfen 041000, China;
Zhihe Li, 2. School of Educational Science, Shanxi Normal University, Linfen 041000, China;

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.

Recommended Citation

Zhang, Huili and Li, Zhihe (2019) "A Cloud Service Composition Optimization Based on HNN," Journal of System Simulation: Vol. 31: Iss. 11, Article 16.
DOI: 10.16182/j.issn1004731x.joss.19-FZ0341
Available at: https://dc-china-simulation.researchcommons.org/journal/vol31/iss11/16

First Page

2335

Revised Date

2019-07-18

DOI Link

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

Last Page

2343

CLC

TP393;TP181

Recommended Citation

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

Corresponding Author

Zhihe Li,

DOI

10.16182/j.issn1004731x.joss.19-FZ0341