电子学报 ›› 2018, Vol. 46 ›› Issue (1): 245-251.DOI: 10.3969/j.issn.0372-2112.2018.01.034

• 科研通信 • 上一篇    下一篇

一种基于任务粒化的服务组合优化方法

张以文, 崔光明, 郭星, 张燕平   

  1. 安徽大学计算智能与信号处理教育部重点实验室, 安徽合肥 230031
  • 收稿日期:2016-10-09 修回日期:2017-02-16 出版日期:2018-01-25 发布日期:2018-01-25
  • 通讯作者: 崔光明
  • 作者简介:张以文,男,1976年出生.安徽大学计算机科学与技术学院副教授,博士,硕士生导师.研究方向为服务计算、大数据.E-mail:zhangyiwen@ahu.edu.cn;郭星,男,1983年出生.安徽大学计算机科学与技术学院讲师,博士.研究方向:机器学习、服务计算、大数据.E-mail:guoxing@ahu.edu.cn;张燕平,女,1962年出生.安徽大学计算机科学与技术学院教授,博士生导师.研究方向为商空间、智能计算.E-mail:zhangyp2@gmail.com
  • 基金资助:
    国家自然科学基金(No.71601001,No.61673020,No.61672386);安徽省高校自然科学基金重点项目(No.KJ2016A038)

A Service Composition Optimization Method Based on Task-Granulating

ZHANG Yi-wen, CUI Guang-ming, GUO Xing, ZHANG Yan-ping   

  1. Anhui University, Key Laboratory of Intelligent Computing & Signal Processing, Ministry of Education, Anhui, Hefei 230031, China
  • Received:2016-10-09 Revised:2017-02-16 Online:2018-01-25 Published:2018-01-25

摘要: 在big service背景下,越来越多的资源以服务的形式发布与使用,用户需求越来越复杂,导致服务组合计算规模呈指数级增长.本文提出一种任务粒化算法(TgA,Task-granular Algorithm),用于快速有效地求解大规模服务组合优化问题.首先,构建任务粒化分层服务组合模型,并分析了该模型的计算复杂性;其次,根据现有QoS属性计算方式,从理论上分析其在任务粒化过程中的可行性;最后,大量仿真实验结果表明,相比于经典的PSO算法,TgA可以将服务组合优化时间性能提高约4至7倍,且寻优精度提高10%以上.

关键词: 服务组合, 任务粒化, 分层模型, PSO

Abstract: Under the background of big service,more and more resources are released and utilized in the form of services.Meanwhile,the users' requirements grow in complexity,which leads to the exponential growth of the service composition calculation scale.In this paper,a task-granulating algorithm called TgA is proposed to solve large-scale service composition optimization problem quickly and effectively.Firstly,we build a hierarchical service composition model based on task granulation,and analyze its computational complexity.Secondly,we analyze the feasibility theoretically during the task-granulating according to the calculation of existing QoS attributes.Finally,a large number of simulation experimental results show that,compared to the classical particle swarm optimization (PSO) algorithm,the proposed algorithm can improve the service composition optimization performance by 4 to 7 times and increase the optimization accuracy by more than 10%.

Key words: service composition (SC), task granulating, hierarchical model, PSO

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