Web service configuration under multiple quality-of-service attributes

With the popularity of Internet technology, web services are becoming the most promising paradigm for distributed computing. However, when a single web service fails to meet service requestor's multiple function needs, web services need to be dynamically configured together to form a web service composition. Since there may be many configurations providing identical functionality with different quality-of-service (QoS), a choice needs to be made according to users' functional and nonfunctional requirements. In this paper, we formulate a web service functional configuration problem by using Petri nets. The graph structure and algebraic properties of the model are analyzed in detail to show that a basis solution of a state-shift equation of the Petri net model corresponds to a realizable configuration process. This result is later used to formulate the multiple attribute QoS optimization problem to a linear programming problem. Finally, a case study is performed to show that the proposed analysis result can be effectively applied in practice. Note to Practitioners-When any single web service fails to accomplish service requestor's multiple function requirements, multiple web services are needed to form a service composition. This work deals with automatic configuration of services under practical constraints. First, according to the customized or applicationspecific web service functional requirement, discover all the web services. Second, build a complete service functional dependency configuration net based on Petri nets. Third, choose and compute the quality-of-service (QoS) attributes for the whole configuration. A transformation method is utilized to change nonlinear aggregation functions to linear ones. Relative importance of different attributes is represented through subjective preference or perception. Fourth, compute the QoS attribute value for each real web service. An association algorithm translates and compiles QoS attributes. Finally, the linear programming problem is set and solved. The best configuration is found and sensitivity analysis is carried out. The concepts and developed algorithms can be readily put into industrial applications.