Learning fairness under constraints: A decentralized resource allocation game

Qinyun Zhu; Jae C. Oh

doi:10.1109/ICMLA.2016.122

Learning fairness under constraints: A decentralized resource allocation game

Qinyun Zhu, Jae C. Oh

Department of Electrical Engineering & Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Scopus citations

Abstract

We study multi-type resource allocation in multiagent system, where some constraints are enforced upon resource providers and users. These constraints are limitations of resource types and connection availabilities, which may make the collaboration between agents infeasible. We discuss the notion of distributed resource fairness under these constraints. Then we propose a game theory and reinforcement learning based solution for collaborative resource allocation, so that resources are assigned to users fairly and tasks are assigned to resource agents efficiently. We utilize data from Google data center as our input to simulations. Results show that our learning approach outperforms a greedy and random explorations in terms of resource utilization and fairness.

Original language	English (US)
Title of host publication	Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	214-221
Number of pages	8
ISBN (Electronic)	9781509061662
DOIs	https://doi.org/10.1109/ICMLA.2016.122
State	Published - Jan 31 2017
Event	15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016 - Anaheim, United States Duration: Dec 18 2016 → Dec 20 2016

Other

Other	15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016
Country/Territory	United States
City	Anaheim
Period	12/18/16 → 12/20/16

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications

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10.1109/ICMLA.2016.122

Cite this

Learning fairness under constraints : A decentralized resource allocation game. / Zhu, Qinyun; Oh, Jae C.

Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 214-221 7838147.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Zhu, Q & Oh, JC 2017, Learning fairness under constraints: A decentralized resource allocation game. in Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016., 7838147, Institute of Electrical and Electronics Engineers Inc., pp. 214-221, 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016, Anaheim, United States, 12/18/16. https://doi.org/10.1109/ICMLA.2016.122

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Learning fairness under constraints: A decentralized resource allocation game

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