We propose a comprehensive framework for adaptivity of service-based applications, which exploits the concept of process fragments as a way to model reusable process knowledge and to allow for the dynamic, incremental, contextaware composition of such fragments into adaptable servicebased applications. The framework provides a set of adaptation mechanisms that, combined through adaptation strategies, are able to solve complex adaptation problems. An implementation of the proposed solution is presented and evaluated on a realworld scenario from the logistics domain.
In the last decade, many approaches to automated service composition have been proposed. However, most of them do not fully exploit the opportunities offered by the Internet of Services (IoS). In this article, we focus on the dynamicity of the execution environment, that is, any change occurring at run-time that might affect the system, such as changes in service availability, service behavior, or characteristics of the execution context. We indicate that any IoS-based application strongly requires a composition framework that supports for the automation of all the phases of the composition life cycle, from requirements derivation, to synthesis, deployment and execution. Our solution to this ambitious problem is an AI planning-based composition framework that features abstract composition requirements and context-awareness. In the proposed approach most human-dependent tasks can be accomplished at design time and the few human intervention required at run time do not affect the system execution. To demonstrate our approach in action and evaluate it, we exploit the ASTRO-CAptEvo framework, simulating the operation of a fully automated IoS-based car logistics scenario in the Bremerhaven harbor.
Automated composition of services is a key functionality for the adoption of the service-oriented development paradigm. Solving this problem in practice requires the ability to consider asynchronous stateful services and to express complex composition requirements which may span different phases of the life-cycle of component services. In this paper we present a novel automated service composition approach which addresses these challenges by associating so-called objects to services, and by introducing a simple yet powerful notation to express composition requirements on them. We recast this view of the problem as a specific form of planning; our experiments on a prototype implementation witness the ability of our approach to deal with realistic scenarios and requirements that cannot be tackled by other current approaches.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.