一种基于HNN的云服务组合优化

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

摘要/Abstract

摘要： 随着云服务应用开发的日新月异,如何有效地在云平台上实现优化服务的组合,提升云平台系统的整体性能是一个亟待解决的研究问题。为提升云服务的效率,提出一种基于霍普菲尔德神经网络的组合优化模型。该方法针对云服务问题建模;设计一种带有柯西扰动技术的PSO算法来改进霍普菲尔德模型,将该云服务问题表达为霍普菲尔德神经网络能量模型进行优化。通过实验比较证明,该方法比其它典型算法可以更加有效地提升云服务组合优化执行的效率。

关键词: 霍普菲尔德神经网络, 组合优化, Web服务, 资源约束, 负载均衡, 云计算

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

中图分类号:

TP393
TP181

张会丽, 李志河. 一种基于HNN的云服务组合优化[J]. 系统仿真学报, 2019, 31(11): 2335-2343.

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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