Autonomic computing: helping computers help themselves
IEEE Spectrum - They might be giants
The Vision of Autonomic Computing
Computer
Making Rational Decisions Using Adaptive Utility Elicitation
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Incremental utility elicitation with minimax regret decision criterion
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Cooperative negotiation in autonomic systems using incremental utility elicitation
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Self-adaptive software: Landscape and research challenges
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Elicitation and utilization of application-level utility functions
ICAC '09 Proceedings of the 6th international conference on Autonomic computing
A geometric approach to find nondominated policies to imprecise reward MDPs
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
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Autonomic (self-managing) computing systems face the critical problem of resource allocation to different computing elements. Adopting a recent model, we view the problem of provisioning resources as involving utility elicitation and optimization to allocate resources given imprecise utility information. In this paper, we propose a new algorithm for regret-based optimization that performs significantly faster than that proposed in earlier work. We also explore new regret-based elicitation heuristics that are able to find near-optimal allocations while requiring a very small amount of utility information from the distributed computing elements. Since regret-computation is intensive, we compare these to the more tractable Nelder-Mead optimization technique w.r.t. amount of utility information required.