QoS-based service selection

随着Web服务的激增，如何识别和选择最符合用户需求的服务成为一个巨大的挑战。通常，在找到功能匹配的服务之后，进一步的选择和排序基于非功能属性，例如服务质量（QoS）。基于QoS的服务选择已经有很多研究工作。然而，还有一些独特的挑战尚未得到充分解决，还有更多的工作要做。在这个建议中，我们想解决其中的一些问题，包括模糊和灵活的QoS要求，服务的动态性，以及相对的用户满意度。我们的目标是设计和实现一个基于QoS的服务选择系统，其中浏览和搜索被认为是集成的选择活动，时间作为服务选择的一个重要因素，并通过学习用户的历史行为来提高体验质量（QoE）。主要包括以下几个方面：1）设计一种交互式QoS浏览机制，使用户能够随时了解当前服务的QoS分布情况，并选择自己关心的领域，以获得更多的细节信息，这有望在一定程度上解决需求模糊和服务动态性问题; 2）定义一种表达性查询语言，评估不同的排序模型以选择最合适的一个，并可能进行一些修改以匹配所提出的查询能力; 3）定义一个QoE模型，该模型考虑了单个用户的独特偏好和满意度曲线，并理解满意度总是一个相对和主观的概念，并使用该模型来改善选择性能。这项建议的主要特点是：1）建议的选择系统，用户可以浏览，以澄清他们的服务质量要求和不同属性之间的平衡，并搜索更明确界定的查询，适当的排名结果和整体改善的满意度水平; 2）培养高素质的人才，深入了解服务选择、QoS和QoE等主题，和使用模式分析，并在系统建设和系统实验方法方面有具体经验。

With the proliferation of web services, how to identify and select services best matching users' requirements has become a big challenge. Usually after functionally matching services have been found, the further selection and ranking are based on non-functional properties such as Quality of Services (QoS). There have been many research works on QoS-based service selection. However, there are a few unique challenges which haven't been fully addressed yet, and more works should be done. In this proposal, we would like to address some of them, including the vague and flexible QoS requirements, the dynamic nature of services, and the relative user satisfaction levels. Our objective is to design and implement a QoS-based service selection system, in which browsing and searching are considered as integrated selection activities, time is taken as an important factor for service selection, and Quality of Experience (QoE) is improved through learning from the user history behavior. There are several major steps involved: 1) design an interactive QoS browsing mechanism so that users could always understand the current QoS distribution of services and choose their concerned areas to get more details, which could hopefully solve the vague requirement and service dynamism problems to an extent; 2) define an expressive query language, evaluate different ranking models to choose a most appropriate one and possibly make some modifications to match with the proposed query capabilities; 3) define a QoE model which considers individual user's unique preferences and satisfaction curves and understands that satisfaction is always a relative and subjective concept, and use this model to improve the selection performance. The key features of this proposal would be: 1) with the proposed selection system, users could browse to clarify their QoS requirements and balance among different attributes, and search with more clearly defined queries, properly ranked results and overall improved satisfaction levels; 2) we will train the highly qualified personnel to gain a deep understanding on topics such as service selection, QoS and QoE, and usage pattern analysis, and they will also have concrete experiences on system building and systematic experimental methods.