Multi-constraint Service Selection Based on Local Approximate Filter
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摘要: 随着Web服务数量与用户需求的不断增长,如何在功能约束和QoS约束下选择Web服务去构建高质量的组合服务已成为Web服务领域的一项重要研究内容。然而,现有大多数Web服务选择方法仅仅解决了在多种QoS约束下选择服务的问题,忽略了服务间的功能性约束。为解决这一问题,该文提出一种基于局部近似过滤的多约束服务选择方法。该方法首先利用候选服务在多种约束下的被选关系,滤除部分不满足约束的服务,并估算每个剩余候选服务的局部适应度;然后,利用文中设计的有向粒子群算法,以候选服务的局部适应度为引导信息搜索出最优方案。实验结果表明了方法的有效性。Abstract: Web service selection is a critical procedure for performance-enhancing in composite service. To find the best services from the candidate services, both the Quality of Service (QoS) requirements and functional requirements should be considered. However, most Web service selection methods are based on the assumption of the independence among candidate services, and ignore the dependency relationship and compatible relationship among candidate services. In practice, composite services emphasize the coordination among these component services. The function of a candidate service in a composite service usually depends on the other optional service. To solve this problem, a multi-constraint service selection method is proposed based on local approximate filter. This method filters out part of the unsatisfied constrain services based on local approximate filter, and estimates the local fitness of each of the rest candidate services, then defines a suitable particle swarm algorithm to search the optimal solutions in the light of the calculated local fitness. Experimental results demonstrate the effectiveness of this method.
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