ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2016, Vol. 53 ›› Issue (12): 2867-2881.doi: 10.7544/issn1000-1239.2016.20150078

• 软件技术 • 上一篇    下一篇

不确定感知的自适应云计算服务组合

任丽芳1,2,王文剑1,许行1   

  1. 1(山西大学计算机与信息技术学院 太原 030006); 2(山西财经大学应用数学学院 太原 030006) (renlf@sxufe.edu.cn)
  • 出版日期: 2016-12-01
  • 基金资助: 
    国家自然科学基金项目(61273291,61673249);山西省回国留学人员科研资助项目(2016-004)

Uncertainty-Aware Adaptive Service Composition in Cloud Computing

Ren Lifang1,2, Wang Wenjian1, Xu Hang1   

  1. 1(School of Computer and Information Technology, Shanxi University, Taiyuan 030006); 2(School of Applied Mathematics, Shanxi University of Finance & Economics, Taiyuan 030006)
  • Online: 2016-12-01

摘要: 云计算服务组合是从众多分布在不同云计算平台上的远程服务中选择合适的组件服务来构建可伸缩的松耦合的增值应用.传统的服务组合方法通常将服务选择与服务组合分阶段进行,由于云计算环境的动态性和服务自身演化的随机性,不能保证选择阶段性能最优的服务在组合服务执行阶段依然是最优的.考虑到云计算环境服务组合的动态性和随机性,建立基于部分可观测Markov决策过程(partially observable Markov decision process, POMDP)的服务组合模型SC_POMDP (service composition based on POMDP),并设计用于模型求解的Q学习算法.SC_POMDP模型在组合服务运行中动态地进行服务质量(quality of service, QoS)最优的组件服务选择,且认为组合服务运行的环境状态是不确定的,同时SC_POMDP考虑了组件服务间的兼容性,可保证服务组合对实际情境的适应性.仿真实验表明,所提出的方法能成功地解决不同规模的服务组合问题,在出现不同比率的服务失效时,SC_POMDP仍然能动态地选择可用的最优组件服务,保证服务组合能成功地执行.与已有方法相比,SC_POMDP方法所选的服务有更优的响应时间和吞吐量,表明SC_POMDP可有效地提高服务组合的自适应性.

关键词: 自适应服务组合, 云计算环境, 不确定感知, 部分可观测Markov决策过程, Q学习算法, 服务质量

Abstract: Cloud computing service composition is to select appropriate component services from numerous of services distributed in different clouds to build scalable loose coupling value-added applications. Traditional service composition methods are usually divided into selection stage and composition stage. Hardly guaranteeing the services with the best performance in the selection stage are still optimal in the execution stage because of the dynamic nature of the cloud computing environment and the stochastic nature of services evolution. Focusing on these two natures of service composition in cloud computing environment, a service composition model is built based on POMDP (partially observable Markov decision process) named as SC_POMDP (service composition based on POMDP), and a Q-learning algorithm is designed to solve the model. SC_POMDP can dynamically select the component services with outstanding QoS (quality of service) during the execution of service composition, which aims to ensure the adaptability of the service composition. Different from most existing methods, the proposed SC_POMDP regards the environment of service composition as being uncertain, and the compatibility between component services is considered, hence SC_POMDP is more in line with the real situation. Simulation experiments demonstrate that the proposed method can successfully solve the problems of service composition in different sizes. Specially, when service failure occurs, SC_POMDP can still select the optimal alternative component services to ensure the successful execution of the composite service. Compared with two existing methods,the selected composite service by SC_POMDP is best in response time and throughput, which reflects the superior adaptation of SC_POMDP.

Key words: adaptive service composition, cloud computing environment, uncertainty-aware, partially observable Markov decision process (POMDP), Q-learning algorithm, quality of service (QoS)

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