# Learning Cooperative Oversubscription for Cloud by Chance-Constrained Multi-Agent Reinforcement Learning

arXiv: https://arxiv.org/abs/2211.11759 · HTML: https://mlsanity.com/paper/2211.11759/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 21 Nov 2022.

## Builds on (most cited first, 9 of 9)

- [Value-Decomposition Networks For Cooperative Multi-Agent Learning](https://mlsanity.com/paper/1706.05296/) (2017)
- [Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider](https://mlsanity.com/paper/2003.03423/) (2020)
- [Model-based Chance-Constrained Reinforcement Learning via Separated Proportional-Integral Lagrangian](https://mlsanity.com/paper/2108.11623/) (2021)
- [Constrained Model-Free Reinforcement Learning for Process Optimization](https://mlsanity.com/paper/2011.07925/) (2020)
- [Model-Based Actor-Critic with Chance Constraint for Stochastic System](https://mlsanity.com/paper/2012.10716/) (2020)
- [Prediction-Based Power Oversubscription in Cloud Platforms](https://mlsanity.com/paper/2010.15388/) (2020)
- [Overcommitment in Cloud Services -- Bin packing with Chance Constraints](https://mlsanity.com/paper/1705.09335/) (2017)
- [On Cloud-based Oversubscription](https://mlsanity.com/paper/1402.4758/) (2014)
- [WordPress on AWS: a Communication Framework](https://mlsanity.com/paper/2007.01823/) (2020)

## Built on by (most cited first, 2 of 2)

- [Scalable Reinforcement Learning for Virtual Machine Scheduling](https://mlsanity.com/paper/2503.00537/) (2025)
- [CloudCons: A Comprehensive End-to-End Benchmark for Cloud Resource Consolidation](https://mlsanity.com/paper/2606.13513/) (2026)

## Abstract (arXiv)

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%~ 86%) under different levels of safety constraints.