Web services composition algorithm based on the location of backup service and probabilistic QoS model

Hua WEN

doi:10.11959/j.issn.1000-0801.2016249

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Web services composition algorithm based on the location of backup service and probabilistic QoS model

research and development | 更新时间：2024-06-05

- Web services composition algorithm based on the location of backup service and probabilistic QoS model
- Telecommunications Science Vol. 32, Issue 10, Pages: 116-125(2016)
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- DOI：10.11959/j.issn.1000-0801.2016249
  CLC： TP391
- Published Online：2016-10，
  
  Published：20 October 2016
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Hua WEN. Web services composition algorithm based on the location of backup service and probabilistic QoS model[J]. Telecommunications science, 2016, 32(10): 116-125.
DOI：

Hua WEN. Web services composition algorithm based on the location of backup service and probabilistic QoS model[J]. Telecommunications science, 2016, 32(10): 116-125. DOI： 10.11959/j.issn.1000-0801.2016249.

摘要

针对工作流服务数的增加过程中最初规划的潜在成本较高以及很多服务组合算法可靠性不足等问题，提出了一种利用备份服务位置和概率服务质量（QoS）模型的服务组合算法。该算法计算服务集群的QoS优化选择，为每个服务包含足够数量的备份服务，且考虑了备份服务位置，以便在单个故障点上进行评估。由于这些备份服务分布均匀，防止了任务失败的发生。对于服务选择问题，采用一种改进的多目标优化（MOO）算法，利用聚类和QoS模型来计算可行解集合。仿真实验采用JMETAL 3.1框架，评估备份服务位置的收益以及算法的可靠性。结果表明，相比于其他MOO算法，提出的算法可靠性更高，从备份服务位置所获得的收益更高。

Abstract

In the process of increasing the number of work-flow services

as the potential cost of the initial planning is high

and the reliability of many service composition algorithms is not enough. A service composition algorithm based on the location of backup service and probabilistic quality-of-service（QoS）model was proposed. A service cluster based QoS optimization selection for each service was computed which contained a sufficient number of backup services. The location of backup service was considered to be evaluated at failure of a single point. Because the backup service was distributed evenly

the occurrence of the failure of the task was presented. For the service selection problem

an improved multiple objective optimization（MOO）algorithm was adopted to calculate the feasible solution set using clustering and QoS model. JMETAL 3.1 framework was used to assess the returns of the backup service location and the reliability of the algorithms. The results show that compared to other MOO algorithms

the proposed algorithm is more reliable and the gain from the backup service position is higher.

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