Autonomic policy adaptation using decentralized online clustering

Abstract

Policies are a powerful means of expressing high-level, goal oriented parameters to manage the behavior of systems and users, and are thus valuable tools for autonomic management. However, the autonomic management of policies is in itself a challenging problem. Particularly, their applicability is typically limited in situations where management actions depend on dynamic system properties, which require adapting policy application thresholds and parameters without modifying absolute policy definition constraints. In this paper, we propose a novel policy definition framework that enables autonomic policy adaptation and proactive policy application, based on the online analysis and characterization of system operation and feedback events. Our approach is supported by a decentralized clustering mechanism and scalable distributed communication platform that together enable the online analysis of events and the efficient generation and enforcement of dynamic policies among distributed system components. We justify, with the evaluation of illustrative scenarios, the need for online data analysis for policy adaptation and the potential benefits of our approach.