Oversubscription is a common practice for improving cloud resource
utilization. It allows the cloud service provider to sell more resources than
the physical limit, assuming not all users would fully utilize the resources
simultaneously. However, how to design an oversubscription policy that improves
utilization while satisfying the some safety constraints remains an open
problem. Existing methods and industrial practices are over-conservative,
ignoring the coordination of diverse resource usage patterns and probabilistic
constraints. To address these two limitations, this paper formulates the
oversubscription for cloud as a chance-constrained optimization problem and
propose an effective Chance Constrained Multi-Agent Reinforcement Learning
(C2MARL) method to solve this problem. Specifically, C2MARL reduces the number
of constraints by considering their upper bounds and leverages a multi-agent
reinforcement learning paradigm to learn a safe and optimal coordination
policy. We evaluate our C2MARL on an internal cloud platform and public cloud
datasets. Experiments show that our C2MARL outperforms existing methods in
improving utilization ($20\%\sim 86\%$) under different levels of safety
constraints.

本文针对云服务商超额订阅资源的使用问题，提出了一种基于概率约束优化和多智能体强化学习的超额订阅策略，实验表明该方法可以在不同安全约束条件下提高资源利用率 (20%~86%)。