Year: 2012
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1514115759