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    康国胜, 刘建勋, 唐明董, 刘小青. 面向多请求的Web服务全局优化选择模型研究[J]. 计算机研究与发展, 2013, 50(7): 1524-1533.
    引用本文: 康国胜, 刘建勋, 唐明董, 刘小青. 面向多请求的Web服务全局优化选择模型研究[J]. 计算机研究与发展, 2013, 50(7): 1524-1533.
    Kang Guosheng, Liu Jianxun, Tang Mingdong, Liu Xiaoqing. Global Optimal Web Service Selection Model for Multiple Service Requests[J]. Journal of Computer Research and Development, 2013, 50(7): 1524-1533.
    Citation: Kang Guosheng, Liu Jianxun, Tang Mingdong, Liu Xiaoqing. Global Optimal Web Service Selection Model for Multiple Service Requests[J]. Journal of Computer Research and Development, 2013, 50(7): 1524-1533.

    面向多请求的Web服务全局优化选择模型研究

    Global Optimal Web Service Selection Model for Multiple Service Requests

    • 摘要: 在大量相似Web服务共存竞争的环境下,基于服务质量的Web服务选择成为服务计算领域的热点问题之一.现有的Web服务选择方法主要研究单个服务请求或多个合作关系的服务请求共同选择某一个服务的情形,未考虑多个独立的服务请求同时请求同种功能服务的互相竞争性.针对该问题,根据Web服务与服务需求之间的匹配度,利用0-1整数规划建立全局优化服务选择模型,并结合实际提出通用可行的解决多请求的全局优化服务选择算法(global optimal service selection for multiple requests, GOSSMR),在保证Web服务需求质量得到满足的情况下,避免过多的请求同时选择同一个服务,做到资源合理利用,避免服务负载失衡,提高系统的性能.仿真实验验证了模型算法的可行性和有效性.

       

      Abstract: Web service selection based on quality of service (QoS) has been one of research focuses in service computing field. Current methods of service selection usually focus on a single service request or multiple service requests for co-selecting a shared service at a time, not considering the competitiveness among multiple independent service requests for the same functional Web services. A global optimal service selection model for multiple service requests, according to the matching degree (between service requests and Web services) and 0-1 integral programming, is proposed to solve the conflicts among service requests. A universal and feasible optimal service selection algorithm, named global optimal service selection for multiple requests (GOSSMR), is proposed to solve the model. Under the condition of meeting QoS requirements of service requests, too many requests selecting the same Web service at the same time can be avoided, thereby optimizing the service resources, avoiding the overload, and improving the performance of the system. The feasibility and effectiveness of the model and algorithm are verified by simulations in our work.

       

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