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) |
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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 | |
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 |
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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