New approaches to optimization and utility elicitation in autonomic computing

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 re-sources as involving utility elicitation and opti-mization to allocate resources given imprecise util-ity information. In this paper, we propose a new algorithm for regret-based optimization that per-forms significantly faster than that proposed in ear-lier work. We also explore new regret-based elic-itation 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 util-ity information required.