Multi-Agent Reinforcement Learning with Shared Policy for Cloud Quota Management Problem

Quota is often used in resource allocation and management scenarios to prevent abuse of resource and increase the efficiency of resource utilization. Quota management is usually fulfilled with a set of rules maintained by the system administrator. However, maintaining these rules usually needs deep domain knowledge. Moreover, arbitrary rules usually cannot guarantee both high resource utilization and fairness at the same time. In this paper, we propose a reinforcement learning framework to automatically respond to quota requests in cloud computing platforms with distinctive usage characteristics for users. Extensive experimental results have demonstrated the superior performance of our framework on achieving a great trade-off between efficiency and fairness.

Recommended citation: Tong Cheng, Hang Dong, Lu Wang, Bo Qiao, Si Qin, Qingwei Lin, Dongmei Zhang, Saravan Rajmohan, and Thomas Moscibroda. 2023. Multi-Agent Reinforcement Learning with Shared Policy for Cloud Quota Management Problem. In Companion Proceedings of the ACM Web Conference 2023 (WWW '23 Companion). Association for Computing Machinery, New York, NY, USA, 391–395. https://doi.org/10.1145/3543873.3584634
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