Web Service Selection for Resolving Conflicting Service Requests

Guosheng Kang, Jianxun Liu, Mingdong Tang, Xiaoqing Frank Liu, K. K. Fletcher
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引用次数: 74

Abstract

Web service selection based on quality of service (QoS) has been a research focus in an environment where many similar web services exist. Current methods of service selection usually focus on a single service request at a time and the selection of a service with the best QoS at the user's own discretion. The selection does not consider multiple requests for the same functional web services. Usually, there are multiple service requests for the same functional web service in practice. In such situations, conflicts occur when too many requesters select the same best web service. This paper aims at solving these conflicts and developing a global optimal service selection method for multiple related service requesters, thereby optimizing service resources and improving performance of the system. It uses Euclidean distance with weights to measure degree of matching of services based on QoS. A 0-1 integral programming model for maximizing the sum of matching degree is created and consequently, a global optimal service selection algorithm is developed. The model, together with a universal and feasible optimal service selection algorithm, is implemented for global optimal service selection for multiple requesters (GOSSMR). Furthermore, to enhance its efficiency, Skyline GOSSMR is proposed. Time complexity of the algorithms is analyzed. We evaluate performance of the algorithms and the system through simulations. The simulation results demonstrate that they are more effective than existing ones.
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解决服务请求冲突的Web服务选择
在存在许多类似Web服务的环境中,基于服务质量(QoS)的Web服务选择一直是一个研究热点。当前的服务选择方法通常集中在一次单个服务请求上,并根据用户自己的判断选择具有最佳QoS的服务。选择不考虑对相同功能web服务的多个请求。通常,在实践中,对于同一个功能性web服务存在多个服务请求。在这种情况下,当太多的请求者选择相同的最佳web服务时,就会发生冲突。本文旨在解决这些冲突,为多个相关的服务请求者开发一种全局最优服务选择方法,从而优化服务资源,提高系统性能。该算法采用带权的欧氏距离度量基于QoS的服务匹配程度。建立了匹配度和最大化的0-1积分规划模型,从而提出了全局最优服务选择算法。该模型结合通用可行的最优服务选择算法,实现了面向多请求者的全局最优服务选择。此外,为了提高其效率,提出了Skyline GOSSMR。分析了算法的时间复杂度。我们通过仿真来评估算法和系统的性能。仿真结果表明,该方法比现有方法更有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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