A Framework for Building Intelligent SLA Negotiation Strategies under Time Constraints

In the context of self-management of SLAs, automated and intelligent negotiation solutions for reaching agreements are a hot research topic, especially for self-organizing distributed systems. Previous work regarding SLA negotiation in grids focuses on devising bargaining models where service providers and consumers can meet and exchange SLA offers and counteroffers. Recent developments in agent research further introduce intelligent strategies for contract negotiation. In this paper we build on the latest developments in agent research to provide a generic framework for strategical negotiating of service level values under time constraints. We exemplify the usage of our generic framework by extending the Bayesian learning agent. We prove that a time-constrained negotiation strategy can surpass the baseline learning scheme in a longer-lasting negotiation, because it gives the intelligent learning scheme time to converge and to estimate better the opponent's profile.