  Decentralized resource allocation is a key problem for large-scale autonomic
(or self-managing) computing systems. Motivated by a data center scenario, we
explore efficient techniques for resolving resource conflicts via cooperative
negotiation. Rather than computing in advance the functional dependence of each
element's utility upon the amount of resource it receives, which could be
prohibitively expensive, each element's utility is elicited incrementally. Such
incremental utility elicitation strategies require the evaluation of only a
small set of sampled utility function points, yet they find near-optimal
allocations with respect to a minimax regret criterion. We describe preliminary
computational experiments that illustrate the benefit of our approach.
