Context-aware automatic service selection

Service-Oriented Architecture (SOA) is a paradigm for developing next generation distributed systems. SOA introduces an opportunity to build dynamically configurable distributed systems by invoking suitable services at runtime, which makes the systems being more flexible to be integrated and easily to be reused. With fast growing numbers of offered services, automatically identifying suitable services becomes a crucial issue. A new and interesting research direction is to select a service which is not only suitable in general but also suitable towards a particular requester's needs and services context at runtime. This dissertation proposes an approach for supporting automatic context-aware service selection and composition in a dynamic environment. The main challenges are: (1) specifying context information in a machine usable form; (2) developing a service selection method which can choose the adequate services by use the context information; (3) introducing context-awareness into the service composition process. To address the challenges, we employ Semantic Web technology for modelling context information and service capabilities to automatically generate service selection criteria at runtime. Meanwhile, a Type-based Logic Scoring Preference Extended (TLE) service selection method is developed to adequately and dynamically evaluate and aggregate the context-aware criteria. In addition, we introduce the composition context and a Backward Composition Context based Service Selection algorithm (BCCbSS) for composing suitable services on the y in a fault-tolerant manner. Furthermore, this dissertation describes the design and implementation of the method and algorithm. Experimental evaluation results demonstrate that the TLE method and BCCbSS algorithm provide an efficient and scalable solution to deal with the context-aware service selection problem both in single service selection and composition scenarios. Our research results make a further step to develop highly automated and dynamically adaptive systems in the future