A Model and Algorithm for Self-Adaptation in Service-oriented Systems

Abstract—In this paper, we address the problem of selfadaptation in internet-scale service-oriented systems. Services need to adapt by select the best neighboring services solely based on local, limited information. In such complex systems, the global significance of the various selection parameters dynamically changes. We introduce a novel metric measuring the distribution and potential impact of service properties affecting such selection parameters. We further present an formalism identifying the most significant properties based on aggregated service interaction data. We ultimately provide a ranking algorithm exploiting these dynamic interaction characteristics. Experimental evaluation demonstrates scalability and adaptiveness of our approach.

• Type: Conference
• Year: 2009
• Where: ECOWS
• Authors: Christoph Dorn, Daniel Schall, Schahram Dustdar
• Keywords: ECOWS 2009, Internet Technology, Internet-scale Service-oriented Systems, Parameters Dynamically Changes, Selection Parameters