一种基于推荐网络和蚁群算法的服务发现方法

A Service Discovery Method Based on Referral Network and Ant-Colony Algorithm

服务分散存储在互联网上,随着互联网上Web服务数量的迅速增长,自动、准确、快速地搜索Web服务已经成为面向服务计算中的难点和关键问题.文中针对在开放、动态环境下现有的服务发现研究中存在的搜索效率不高、负载不均衡和语义欠缺等问题,提出了一种基于推荐网络和蚁群算法的服务发现方法.首先,该方法构建一个自组织服务推荐网络模型,并给出了相关策略,从而适合大规模开放、动态的网络环境,为服务发现提供了搜索空间和基础.其次,在自组织服务推荐网络模型的基础上,给出了一种基于蚁群算法的服务发现方法,该方法使用推荐有效地提高了服务发现的成功率和查全率,引入蚁群算法的思想有效地解决了服务发现中的网络负载均衡问题.最后,实验结果证明了该方法的正确性和可行性.

Services are archived dispersively on Internet.With the rapid increasing number of Web services,how to discovery the desired Web service automatically and accurately has been a critical hot issue in service-oriented computing.Aiming at such problems as the low search efficiency,load imbalance and lacking semantic understanding existing in current service discovery researches,this paper proposes a new service discovery method based on referral network and ant-colony algorithm.Firstly,a self-organizing service referral network model-SSRNM is constructed.SSRNM defines related policies and provides the search space for service discovery,thus it can be fit for the dynamic and open environment.Secondly,based on SSRNM,an ant-colony algorithm-based service discovery method ABSDA is presented.This method makes use of recommendation to improve the success and recall rate of service discovery,and adopts the idea of ant colony algorithm to solve the network load balancing problem effectively.Finally,the experimental results prove that this method is correct and feasible.