Cooperative Negotiation in Autonomic Systems using Incremental Utility Elicitation

TL;DR

This paper presents a method for decentralized resource allocation in large-scale autonomic systems using incremental utility elicitation, enabling efficient and near-optimal negotiation without exhaustive utility computation.

Contribution

It introduces an incremental utility elicitation technique for cooperative negotiation, reducing computational costs while achieving near-optimal resource allocations.

Findings

Near-optimal allocations achieved with limited utility sampling

Incremental elicitation reduces computational overhead

Preliminary experiments demonstrate effectiveness

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.