A Service Composition Optimization Method Based on Task-Granulating

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

doi:10.3969/j.issn.0372-2112.2018.01.034

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A Service Composition Optimization Method Based on Task-Granulating

更新时间：2025-07-16

- A Service Composition Optimization Method Based on Task-Granulating
- Acta Electronica Sinica Vol. 46, Issue 1, Pages: 245-251(2018)
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.71601001, No.61673020, No.61672386);Key Program of Natural Science Foundation for Colleges and Universities in Anhui Province (No.KJ2016A038)
- DOI：10.3969/j.issn.0372-2112.2018.01.034
  CLC： TP301
- Published Online：25 January 2018，
  
  Published：2018
- 稿件说明：
移动端阅览
ZHANG Yi-wen, CUI Guang-ming, GUO Xing, et al. A Service Composition Optimization Method Based on Task-Granulating[J]. Acta Electronica Sinica, 2018, 46(1): 245-251.
DOI：

ZHANG Yi-wen, CUI Guang-ming, GUO Xing, et al. A Service Composition Optimization Method Based on Task-Granulating[J]. Acta Electronica Sinica, 2018, 46(1): 245-251. DOI： 10.3969/j.issn.0372-2112.2018.01.034.

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.

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%.

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