Distributed Scheduling of Tasks with Deadlines and Resource Requirements
IEEE Transactions on Computers
The dynamics of reinforcement learning in cooperative multiagent systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Cluster reserves: a mechanism for resource management in cluster-based network servers
Proceedings of the 2000 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Confidence Based Dual Reinforcement Q-Routing: An adaptive online network routing algorithm
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Sharc: Managing CPU and Network Bandwidth in Shared Clusters
IEEE Transactions on Parallel and Distributed Systems
Online resource allocation using decompositional reinforcement learning
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Adaptive load balancing: a study in multi-agent learning
Journal of Artificial Intelligence Research
Self-organization for coordinating decentralized reinforcement learning
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
A novel multi-agent reinforcement learning approach for job scheduling in Grid computing
Future Generation Computer Systems
Cognitive policy learner: biasing winning or losing strategies
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
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Resource allocation in computing clusters is traditionally centralized, which limits the cluster scale. Effective resource allocation in a network of computing clusters may enable building larger computing infrastructures. We consider this problem as a novel application for multiagent learning (MAL). We propose a MAL algorithm and apply it for optimizing online resource allocation in cluster networks. The learning is distributed to each cluster, using local information only and without access to the global system reward. Experimental results are encouraging: our multiagent learning approach performs reasonably well, compared to an optimal solution, and better than a centralized myopic allocation approach in some cases.