Automated Generation of Knowledge Plane Components for Multimedia Access Networks
MACE '08 Proceedings of the 3rd IEEE international workshop on Modelling Autonomic Communications Environments
A reinforcement learning based self-healing algorithm for managing context adaptation
Proceedings of the 12th International Conference on Information Integration and Web-based Applications & Services
Enacting SLAs in clouds using rules
Euro-Par'11 Proceedings of the 17th international conference on Parallel processing - Volume Part I
Energy-efficient and SLA-aware management of IaaS clouds
Proceedings of the 3rd International Conference on Future Energy Systems: Where Energy, Computing and Communication Meet
Adaptive resource configuration for Cloud infrastructure management
Future Generation Computer Systems
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The use of policies within autonomic computing has received significant interest in the recent past. Policy-driven management offers significant benefit since it makes it more straight forward to define and modify systems behavior at run-time, through policy manipulation, rather than through re-engineering. In this paper, we present an adaptive policy-driven autonomic management system which makes use of reinforcement learning methodologies to determine how to best use a set of active policies to meet different performance objectives. The focus, in particular, is on strategies for adapting what has been learned for one set of policy actions to a "similar'' set of policies when run-time policy modifications occur. We illustrate the impact of the adaptation strategies on the behavior of a multi-component Web server.