Learning-based negotiation strategies for grid scheduling

One of the key requirements for grid infrastructures is the ability to share resources with nontrivial qualities of service. However, resource management in a decentralized infrastructure is a complex task as it has to cope with different policies and objectives of the different resource providers and the resource users. Recent research indicates that agreement-based resource management will solve many of these problems as it supports the reliable interaction between different providers and users. Here, negotiation is needed to create such bi-lateral agreements between grid parties. Such negotiation processes should be automated with no or minimal human interaction, considering the potential scale of grid systems and the amount of necessary transactions. Therefore, strategic negotiation models play an important role. In this paper, a negotiation model and learning-based negotiation strategies are proposed and examined. Simulations have been conducted to evaluate the presented system. The results demonstrate that the proposed negotiation model and the learning based negotiation strategies are suitable and effective for grid environments.