Multi-user web service selection based on multi-QoS prediction
Information Systems Frontiers
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Web service selection based on quality of service (QoS) has been a research focus in an environment where many similar web services exist. Current methods of service selection usually focus on a single service request at a time and the selection of a service with the best QoS at the user's own discretion. The selection does not consider multiple requests for the same functional web services. Usually, there are multiple service requests for the same functional web service in practice. In such situations, conflicts occur when too many requesters select the same best web service. This paper aims at solving these conflicts and developing a global optimal service selection method for multiple related service requesters, thereby optimizing service resources and improving performance of the system. It uses Euclidean distance with weights to measure degree of matching of services based on QoS. A 0-1 integral programming model for maximizing the sum of matching degree is created and consequently, a global optimal service selection algorithm is developed. The model, together with a universal and feasible optimal service selection algorithm, is implemented for global optimal service selection for multiple requesters (GOSSMR). Furthermore, to enhance its efficiency, Skyline GOSSMR is proposed. Time complexity of the algorithms is analyzed. We evaluate performance of the algorithms and the system through simulations. The simulation results demonstrate that they are more effective than existing ones.