基于QoS的Web服务发现与选择的机器学习方法
Qos-Based Web Service Discovery And Selection Using Machine Learning
摘要：
在服务计算中，相同的目标功能可以由来自不同提供者的多个Web服务来实现。由于功能上的相似性，客户需要考虑非功能性标准。然而，由开发者提供的服务质量遭受稀缺性和可靠性的缺乏。此外，服务提供商的声誉是选择服务的重要因素，特别是那些经验不足的人。以前的研究大多集中在用户的反馈，以证明选择。不幸的是，并不是所有的用户都提供反馈，除非他们对服务有非常好或不好的体验。在这篇愿景论文中，我们提出了一个新的架构的Web服务发现和选择。核心部分是一个基于机器学习的方法，使用源代码度量来预测QoS属性。可信度值和先前使用计数用于确定服务的信誉。
英文摘要：
In service computing, the same target functions can be achieved by multiple Web services from different providers. Due to the functional similarities, the client needs to consider the non-functional criteria. However, Quality of Service provided by the developer suffers from scarcity and lack of reliability. In addition, the reputation of the service providers is an important factor, especially those with little experience, to select a service. Most of the previous studies were focused on the user's feedbacks for justifying the selection. Unfortunately, not all the users provide the feedback unless they had extremely good or bad experience with the service. In this vision paper, we propose a novel architecture for the web service discovery and selection. The core component is a machine learning based methodology to predict the QoS properties using source code metrics. The credibility value and previous usage count are used to determine the reputation of the service.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
