ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2018, Vol. 55 ›› Issue (6): 1345-1355.doi: 10.7544/issn1000-1239.2018.20170234

• 软件技术 • 上一篇    

任务粒化的质量约束感知服务组合

张以文1,2,崔光明2,严远亭2,赵姝1,2,张燕平1,2   

  1. 1(计算智能与信号处理教育部重点实验室(安徽大学) 合肥 230031); 2(安徽大学计算机科学与技术学院 合肥 230601) (zhangyiwen@ahu.edu.cn)
  • 出版日期: 2018-06-01
  • 基金资助: 
    国家科技支撑计划基金项目(2015BAK24B01);国家自然科学基金项目(71601001,61673020);教育部人文社会科学基金项目(15YJAZH112);安徽省高校自然科学基金重点项目(KJ2016A038);安徽省自然科学基金项目(1808085MF197)

Quality Constraints-Aware Service Composition Based on Task Granulating

Zhang Yiwen1,2, Cui Guangming2, Yan Yuanting2, Zhao Shu1,2, Zhang Yanping1,2   

  1. 1(Key Laboratory of Intelligent Computing and Signal Processing (Anhui University), Ministry of Education, Hefei 230031); 2(School of Computer Science and Technology, Anhui University, Hefei 230601)
  • Online: 2018-06-01

摘要: 随着服务计算的发展,越来越多的资源以服务的形式发布与使用,服务提供商间的竞争日趋激烈,合作共赢成为必然趋势,但考虑质量约束关系服务组合优化问题复杂性大大增强.为解决这一问题,在充分考虑候选服务间质量约束的同时,对服务组合业务流程进行任务粒化,提出基于任务粒化的质量约束感知服务组合优化方法(Tg-QcA).首先,通过理论分析,验证每种QoS聚合方式均具有子模态性质以及多属性服务组合问题的效用函数仍具有子模态性质,保证了基于任务粒化优化方法的完备性;其次,通过质量约束建模,利用任务间的隶属度进行任务粒化划分,对原问题进行分解,有效降低了问题求解规模;最后,大量的仿真模拟实验结果表明:所提模型与算法具有很好的可行性、高效性和稳定性.

关键词: 服务组合, 任务粒化, 质量约束, 子模, 服务质量

Abstract: With the development of service computing, more and more sources are released and utilized as services. Competition between service providers grows increasingly fierce. Hence, the win-win cooperation between services becomes an inevitable trend. Moreover, the consideration of the quality constraint correlation between businesses further complicates the service composition optimization problem. To solve it, this paper uses task granulation on service composition business process, and presents a quality constraint-aware service composition method based on task granulation (Tg-QcA) when considering the quality constraint between candidate services. Firstly, this paper makes theoretical analysis and verifies that each QoS aggregation has component mode and the utility function of multi-attribute service composition problem still has component mode, thereby guaranteeing the completeness of task-granulation optimization method. Secondly, a quality constraint based model is built and then a task granulation partition is made through the subjection degree between tasks to decompose the original problem, thereby reducing the solving scale of problem. Finally, it is demonstrated by computer simulation that this algorithm and this model have strong feasibility, efficiency and stability.

Key words: service composition, task granulating, quality constraints, submodular, quality of service (QoS)

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