Toward autonomic web services trust and selection

Abstract

Emerging Web services standards enable the development of large-scale applications in open environments. In particular, they enable services to be dynamically bound. However, current techniques fail to address the critical problem of selecting the right service instances. Service selection should be determined based on user preferences and business policies, and consider the trustworthiness of service instances.We propose a multiagent approach that naturally provides a solution to the selection problem. This approach is based on an architecture and programming model in which agents represent applications and services. The agents support considerations of semantics and quality of service (QoS). They interact and share information, in essence creating an ecosystem of collaborative service providers and consumers. Consequently, our approach enables applications to be dynamically configured at runtime in a manner that continually adapts to the preferences of the participants. Our agents are designed using decision theory and use ontologies. We evaluate our approach through simulation experiments.

Reputation-Based Service Level Agreements for Web Services

“…In the same spirit, [9] proposes verity, a QoS measure that takes into account both reputation and the terms of the SLA. [12] and [1] propose concrete frameworks for service selection based on provider reputation.…”

Interaction-Driven Self-adaptation of Service Ensembles

“…Specifically in service-oriented systems, extensive research efforts focus on service selection based on Quality-of-Service (QoS) attributes (e.g., [27][28][29]), context information (e.g., [30,31]), or trust (e.g., [32][33][34]). Recently, Skopik et al [35] extended the notion of trust to cover both humans and services in mixed service-oriented systems.…”

An Extensible Monitoring Framework for Measuring and Evaluating Tool Performance in a Service-Oriented Architecture

“…Ran proposes a service discovery model including QoS as constraints for service selection, relying on third-party QoS certification [21]. Maximilien proposes an ontology for modelling QoS and an architecture where agents stand between providers and consumers and aggregate QoS experience on behalf of the consumers [17]. Erradi presents a middleware solution for monitoring composite web service performance and other quality criteria at the message level [7].…”