Service-oriented computing (SOC) is an interdisciplinary paradigm that revolutionizes The topic of this paper is the development of an agent based approach for Web services discovery and selection in witch, OWL-S is used to describe Web services, QoS and service customer request. We develop an efficient semantic service matching which takes into account concepts properties to match concepts in

Web services are useless if they cannot be discovered. So, discovery is the most important task in the Web service model.Recent researchers have focused on performing semantic matching to enhance the accuracy of Web service discovery.In this paper we present a framework for Web services discovery and selection based on intelligent software agents, OWLS and domain ontologies. With the help of software agents, information provided by Web services can be made more efficient and more dynamic.With semantics provided by OWLS and domain concepts, match and discovery engine can return the most relevant services.

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Service-oriented architecture provides the ability to combine several web services in order to fulfil a user-specific requirement. In dynamic environments, the appearance of several unforeseen events can destabilize the composite web service (CWS) and affect its quality. To deal with these issues, the composite web service must be dynamically reconfigured. Dynamic reconfiguration may be enhanced by avoiding the invocation of degraded web services by predicting QoS for the candidate web service. In this paper, we propose a dynamic reconfiguration method based on HMM (Hidden Markov Model) states to predict the imminent degradation in QoS and prevent the invocation of partner web services with degraded QoS values. PSO (Particle Swarm Optimization) and SFLA (Shuffled Frog Leaping Algorithm) are used to improve the prediction efficiency of HMM. Through extensive experiments on a real-world dataset, WS-Dream, the results demonstrate that the proposed approach can achieve better prediction accuracy. Moreover, we carried out a case study where we revealed that the proposed approach outperforms several state-of-the-art methods in terms of execution time.