Service Level Achievements -- Distributed Knowledge for Optimal Service Selection

Abstract

In a service-oriented setting, where services are composed to provide end user functionality, it is a challenge to find the service components with best-fit functionality and quality. A decision based on information mainly provided by service providers is inadequate as it cannot be trusted in general. In this paper, we discuss service compositions in an open market scenario where an automated best-fit service selection and composition is based on Service Level Achievements instead. Continuous monitoring updates the actual Service Level Achievements which can lead to dynamically changing compositions. Measurements of real life services exemplify the approach.

Optimization of Business Process Execution in Services Architecture: A Systematic Literature Review

“…In [117] Andersson et al present a framework for run-time service composition. In this framework, the original Service Composition optimization algorithm is presented together with a proposition on how to monitor service providers' performance during and selection rules during the system's life cycle to update selected service instances.…”

Evaluation of the Employment of Machine Learning Approaches and Strategies for Service Recommendation

“…In such a case, not all NFPs are equally important, so their importance has to be weighted and taken into account [2]. In [1], we introduced our framework which optimizes service selection based on consumer experience, call context, and preferences (utility). Within this paper, the focus is set on the machine learning approaches which can be employed for service recommendation in general, and which can be implemented within our framework.…”

“…The measurement data was gained from four real-world stock quote Web services [1]. The services are functionally similar, so they can be substituted.…”

“…Our service recommendation framework uses a collaborative knowledge base of consumption experiences of similar consumers in the past to predict the performance of a service in a certain consumer-based call context. This is used to recommend the best-fit service candidate to a consumer, considering his/her preferences [1,2]. Since the experienced performance at consumer side is influenced by a consumer's call context, e. g., calling time and location, the performance has to be predicted based on this context.…”