Technical Note: \cal Q-Learning
Machine Learning
The AppLeS parameter sweep template: user-level middleware for the grid
Proceedings of the 2000 ACM/IEEE conference on Supercomputing
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Adaptive Computing on the Grid Using AppLeS
IEEE Transactions on Parallel and Distributed Systems
Simgrid: A Toolkit for the Simulation of Application Scheduling
CCGRID '01 Proceedings of the 1st International Symposium on Cluster Computing and the Grid
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
A grid service broker for scheduling distributed data-oriented applications on global grids
MGC '04 Proceedings of the 2nd workshop on Middleware for grid computing
Concurrency and Computation: Practice & Experience - Middleware for Grid Computing
Learning-Based Negotiation Strategies for Grid Scheduling
CCGRID '06 Proceedings of the Sixth IEEE International Symposium on Cluster Computing and the Grid
New grid scheduling and rescheduling methods in the GrADS project
International Journal of Parallel Programming - Special issue: The next generation software program
A Grid Scheduling Algorithm for Bag-of-Tasks Applications Using Multiple Queues with Duplication
ICIS-COMSAR '06 Proceedings of the 5th IEEE/ACIS International Conference on Computer and Information Science and 1st IEEE/ACIS International Workshop on Component-Based Software Engineering,Software Architecture and Reuse
GRAND: toward scalability in a Grid environment: Research Articles
Concurrency and Computation: Practice & Experience - Middleware for Grid Computing: A “Possible Future”
Evaluation of Coordinated Grid Scheduling Strategies
HPCC '09 Proceedings of the 2009 11th IEEE International Conference on High Performance Computing and Communications
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Grid environments are dynamic and heterogeneous by nature, therefore requiring adaptive scheduling strategies. Reinforcement learning is an interesting and simple adaptive approach that may work well in actual grid environments. In this work, we employ reinforcement learning to classify available resources in a grid environment, giving support to two scheduling algorithms, AG and MQD. We study the makespan optimisation and load balancing. An algorithm known as RR is used for normalising purposes.