Towards the Application of Reinforcement Learning Techniques for Quality-Based Service Selection in Automated Service Composition

Author

Jungmann, Alexander ; Kleinjohann, Bernd

Author_Institution

Cooperative Comput. & Commun. Lab. (C-Lab.), Univ. of Paderborn, Paderborn, Germany

fYear

2012

fDate

24-29 June 2012

Firstpage

701

Lastpage

702

Abstract

A major goal of the On-The-Fly Computing project is the automated composition of individual services based on services that are available in dynamic markets. Dependent on the granularity of a market, different alternatives that satisfy the requested functional requirements may emerge. In order to select the best solution, services are usually selected with respect to their quality in terms of inherent non-functional properties. In this paper, we describe our idea of how to model this service selection process as a Markov Decision Process, which we in turn intend to solve by means of Reinforcement Learning techniques in order to control the underlying service composition process. In addition, some initial issues with respect to our approach are addressed.

Keywords

Markov processes; learning (artificial intelligence); quality of service; software quality; Markov decision process; automated service composition; functional requirements; on-the-fly computing project; quality-based service selection; reinforcement learning; Conferences; Context; Globalization; Learning; Markov processes; Optimization; Process control; Markov Decision Process; On-The-Fly Computing; Reinforcement Learning; Service Composition; Service Selection;

fLanguage

English

Publisher

ieee

Conference_Titel

Services Computing (SCC), 2012 IEEE Ninth International Conference on

Conference_Location

Honolulu, HI

Print_ISBN

978-1-4673-3049-7

Type

conf

DOI

10.1109/SCC.2012.76

Filename

6274219