Service discovery method based on two-step clustering
Jiajing He, Jindong Wang, Na Wang, Niu Kan · 2015
With the explosive growth number of services in cloud computing environment, how to accurately and rapidly discover the services that can meet user's functional and nonfunctional requirements is a challenging subject. Aiming at issues of service inefficiencies and low precision in the existing service discovery methods, a model for service discovery based on functions and QoS clustering is proposed. This model clusters services separately from the functional level and the QoS level and two clustering algorithms are proposed respectively. Finally, the services meeting customer's needs are selected from both the functional and non-functional levels with the similarity between them, and service discovery is achieved. Experimental results demonstrate that the method is effective, which can improve the accuracy and efficiency of the service discovery process.