QoS Requirement Self-learning Based Dynamic Adaptive Web Service Selection in Ami Environment

摘要

Given the rapidly increasing number of available Web services and the different user QoS requirement, selecting a suitable service for each user adaptively is difficulty in Anil environment. In this paper, we propose a backward inference based user QoS requirements self-learning model to learn the users' special preferences by using the service evaluation information and then construct a user's QoS cognitive set by means of learned information. We also put forward a dynamic adaptive service selection algorithm based on the dynamic multi-attribute decision making theory. The algorithm, firstly, can generate users QoS requirement preferences according to the information of candidate services and user's QoS cognitive set. Then it automatic selects a suitable service for user by using the services' history information, service providers' statements values and user QoS requirements. Last., we verify the usability of the model through prototypical implementation and explain its advantage and deficiency.