Future Generation Computer Systems - Special issue on metacomputing
Automatic Learning Techniques in Power Systems
Automatic Learning Techniques in Power Systems
Machine Learning
JSSPP '02 Revised Papers from the 8th International Workshop on Job Scheduling Strategies for Parallel Processing
User Preference Driven Multiobjective Resource Management in Grid Environments
CCGRID '01 Proceedings of the 1st International Symposium on Cluster Computing and the Grid
Predictive Application-Performance Modeling in a Computational Grid Environment
HPDC '99 Proceedings of the 8th IEEE International Symposium on High Performance Distributed Computing
Grid resource management: state of the art and future trends
Grid resource management: state of the art and future trends
The Grid 2: Blueprint for a New Computing Infrastructure
The Grid 2: Blueprint for a New Computing Infrastructure
Brain Meets Brawn: Why Grid and Agents Need Each Other
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Resource Allocation in the Grid Using Reinforcement Learning
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Usage Policy-Based CPU Sharing in Virtual Organizations
GRID '04 Proceedings of the 5th IEEE/ACM International Workshop on Grid Computing
Balancing Risk and Reward in a Market-Based Task Service
HPDC '04 Proceedings of the 13th IEEE International Symposium on High Performance Distributed Computing
Proceedings of the 2nd international conference on Service oriented computing
The Anatomy of the Grid: Enabling Scalable Virtual Organizations
International Journal of High Performance Computing Applications
The GrADS Project: Software Support for High-Level Grid Application Development
International Journal of High Performance Computing Applications
Artificial Intelligence and Grids: Workflow Planning and Beyond
IEEE Intelligent Systems
GRUBER: a grid resource usage SLA broker
Euro-Par'05 Proceedings of the 11th international Euro-Par conference on Parallel Processing
| Hi-index | 0.00 |
Grid computing can be defined as coordinated resource sharing and problem solving in dynamic, multiinstitutional collaborations [1]. As more Grids are deployed worldwide, the number of multi-institutional collaborations is rapidly growing. However, for Grid computing to realize its full potential, it is expected that Grid participants are able to use one another resources. Resource negotiation (i.e. exchange or trading of resources between Grids) enables Grid participants to face an unstable request environment.The aim of this position paper is to present a survey of the current state and challenges of resource negotiation research, with a Machine Learning perspective. We support the view that negotiation and learning are intrinsically linked. In particular, we show the expected benefits of integrating Machine Learning techniques with resource negotiation.