Learning Optimal Web Service Selections in Dynamic Environments when Many Quality-of-Service Criteria Matter

Abstract

The emergence of the World Wide Web has lead to growing needs for interacting components capable of achieving – together – complex requests on the Web. Service oriented Systems (SoS) are a response to this issue, given available standards for describing individual services and interaction between them, and the attention to interoperability combined with an uptake in industry. A service is a self-describing and self-contained modular application designed to execute a well-delimited task, and that can be described, published, located, and invoked over a network (McIlraith & Martin, 2003; Papazoglou & Georgakopoulos, 2003). A web service is a service made available on the Internet via tailored technologies such as WSDL, SOAP or UDDI (Walsh, 2002). To fulfill elaborate requests that involve many execution steps, web services participate in web services compositions. To optimize such compositions, each step of the execution is achieved by the most competitive available web service. The most competitive web service is the one who performs the given task while fulfilling its functional requirements and providing the best observed values of quality of service (QoS). QoS are the nonfunctional properties of a web service and refer to concerns such as availability, reliability, cost or security (Menasce, 2002). The selection of all web services that can participate in a composition (i.e., web services that will perform at least one step of the execution) is under the responsibility of the service composer. To achieve QoS-aware service selection, we rely on a Multi-Criteria Randomized Reinforcement Learning approach (MCRRL). MCRRL authorizes automated continuous optimization of service monitoring and leads the system to respond to the variation of the availability of web services without human involvement. This paper focuses on the composition of services under the constraint of openness, resource distribution, and adaptability to changing web service availability w.r.t. multiple criteria. To enable such system characteristics, a fit between the system architecture and services composition behavior is needed, that is: (1) To support openness, few assumptions can be O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg