QoS-based service selection

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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.