A Multi-Agent Learning Approach to Online Distributed Resource Allocation
Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence. Pages 361-365.
Abstract
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.Chongjie Zhang, Victor Lesser, Prashant Shenoy
@inproceedings{ijcai2009p68,
title = {A Multi-Agent Learning Approach to Online Distributed Resource Allocation},
author = {Zhang, Chongjie and Lesser, Victor R. and Shenoy, Prashant J.},
booktitle = {Proceedings of the Twenty-First International Joint Conference on
Artificial Intelligence, {IJCAI-09}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Hiroaki Kitano},
pages = {361--365},
year = {2009},
url = {https://ijcai.org/proceedings/2009/68},
}
TY - CPAPER TI - A Multi-Agent Learning Approach to Online Distributed Resource Allocation AU - Zhang, Chongjie AU - Lesser, Victor R. AU - Shenoy, Prashant J. T2 - Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence, IJCAI-09 PB - International Joint Conferences on Artificial Intelligence PY - 2009 SP - 361 EP - 365 UR - https://ijcai.org/proceedings/2009/68 ER -Copied
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