Optimized Service Discovery Using QoS Based Ranking: A Fuzzy Clustering and Particle Swarm Optimization Approach

Optimized Service Discovery Using QoS Based Ranking: A Fuzzy Clustering and Particle Swarm Optimization Approach Web services are the key technologies for the web applications developed using Service Oriented Architecture (SOA). Many outsourced web services can be combined to provide value added services to the users. There are many challenges involved in its implementations. One of the essential challenges is service discovery which involves finding a set of suitable web service candidates faster. When a large number of functionally-equivalent services have been discovered, it is difficult for users to choose which one is to be invoked. Researchers have proposed various techniques for service discovery like ranking the web services based on their Quality of Service (QoS). The various parameters of quality are reliability, security, performance, etc. This paper presents an algorithm for building a rule based model for ranking the web services based on quality of service (QoS) using fuzzy clustering and particle swarm optimization (PSO). In general, the numbers of rules are directly proportional to the number of quality attributes considered for ranking but PSO reduces the number of rules by removing the rules that are having less weight age and will not affect the system. This paper also proposes a new web service reference that behaves as an expert system. It contains a rule base and a reference engine. The rule base consists of all the rules and a reference engine that triggers all these rules and gives the rank of the service as an output.