计算机工程与应用 ›› 2010, Vol. 46 ›› Issue (15): 72-76.DOI: 10.3778/j.issn.1002-8331.2010.15.022

• 网络、通信、安全 • 上一篇    下一篇

面向QoS全局优化的大规模Web服务组合方法

吴明晖1,2,熊向辉1,2,应 晶1,2   

  1. 1.浙江大学 计算机科学与技术学院,杭州 310027
    2.浙江大学城市学院 计算机科学与工程学系,杭州 310015
  • 收稿日期:2009-10-15 修回日期:2009-11-23 出版日期:2010-05-21 发布日期:2010-05-21
  • 通讯作者: 吴明晖

QoS-oriented global optimization approach for mass Web services composition

WU Ming-hui1,2,XIONG Xiang-hui1,2,YING Jing1,2   

  1. 1.College of Computer Science and Technology,Zhejiang University,Hangzhou 310027,China
    2.Department of Computer Science and Engineering,Zhejiang University City College,Hangzhou 310015,China
  • Received:2009-10-15 Revised:2009-11-23 Online:2010-05-21 Published:2010-05-21
  • Contact: WU Ming-hui

摘要: 把多个简单Web服务组合成为更强大的组合Web服务是面向服务计算的目标之一。由于存在多个功能相同但服务质量属性不同的候选Web 服务,因此需要针对服务质量要求进行服务组合。鉴于Web服务组合规模的不断增长和特定领域的时限要求,面向实时大规模Web服务组合问题的快速收敛算法尤为重要,然而目前相关工作还很少。论文提出一种新的Web服务组合算法GAELS(Genetic Algorithm Embedded Local Searching),运用高适应度初始种群和局部搜索的变异策略,加快收敛速度。通过实验评测表明与简单遗传算法相比,GAELS算法能更快得到近似最优解,且随着服务规模增长,拥有更好的适应性。

关键词: Web服务组合, QoS全局优化, 遗传算法, 局部搜索

Abstract: One of the aims of SOA is to compose atomic Web services into a powerful composite service.QoS based selection approaches are used to choose the best solution among candidate services with the same functionality.Due to the increasing scale of the candidate Web services and real-time demands of specific application domain,the rapid convergent algorithm for mass Web services composition is special important.However,rare work has been done to solve the problem.The paper proposes a new algorithm named GAELS(Genetic Algorithm Embedded Local Searching),which uses the strategies of high-fitness initial population and mutation with local searching to speed the convergence.The in-depth experimental results show that the proposed GAELS algorithm can get the approximately optimal solution more quickly and be more adaptive to the expanding of candidate services than simple genetic algorithm.

Key words: Web services composition, QoS global optimal, genetic algorithm, local searching

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