Rational Bidding Using Reinforcement Learning
GECON '08 Proceedings of the 5th international workshop on Grid Economics and Business Models
SORMA --- Business Cases for an Open Grid Market: Concept and Implementation
GECON '08 Proceedings of the 5th international workshop on Grid Economics and Business Models
Towards a general model of the multi-criteria workflow scheduling on the grid
Future Generation Computer Systems
Q-Strategy: A Bidding Strategy for Market-Based Allocation of Grid Services
OTM '08 Proceedings of the OTM 2008 Confederated International Conferences, CoopIS, DOA, GADA, IS, and ODBASE 2008. Part I on On the Move to Meaningful Internet Systems:
Negotiation Model Supporting Co-Allocation for Grid Scheduling
GRID '06 Proceedings of the 7th IEEE/ACM International Conference on Grid Computing
WSEAS Transactions on Systems and Control
Grid scheduling optimization under conditions of uncertainty
NPC'07 Proceedings of the 2007 IFIP international conference on Network and parallel computing
New challenges of parallel job scheduling
JSSPP'07 Proceedings of the 13th international conference on Job scheduling strategies for parallel processing
Applying reinforcement learning to scheduling strategies in an actual grid environment
International Journal of High Performance Systems Architecture
Autonomous Agents and Multi-Agent Systems
Negotiation strategies considering opportunity functions for grid scheduling
Euro-Par'07 Proceedings of the 13th international Euro-Par conference on Parallel Processing
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One of the key requirement 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.