# A Survey on Machine Learning for Geo-Distributed Cloud Data Center Management

arXiv: https://arxiv.org/abs/2205.08072 · HTML: https://mlsanity.com/paper/2205.08072/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 55 references that are in the map.
- First posted on arXiv 17 May 2022.

## Builds on (most cited first, 5 of 5)

- [Deep Reinforcement Learning-based Methods for Resource Scheduling in Cloud Computing: A Review and Future Directions](https://mlsanity.com/paper/2105.04086/) (2021)
- [Energy and Network Aware Workload Management for Geographically Distributed Data Centers](https://mlsanity.com/paper/2106.00066/) (2021)
- [Machine Learning (ML)-Centric Resource Management in Cloud Computing: A Review and Future Directions](https://mlsanity.com/paper/2105.05079/) (2021)
- [Machine learning for cloud resources management -- An overview](https://mlsanity.com/paper/2101.11984/) (2021)
- [An Intelligent Resource Reservation for Crowdsourced Live Video Streaming Applications in Geo-Distributed Cloud Environment](https://mlsanity.com/paper/2106.02420/) (2021)

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

- [Game-Theoretic Deep Reinforcement Learning to Minimize Carbon Emissions and Energy Costs for AI Inference Workloads in Geo-Distributed Data Centers](https://mlsanity.com/paper/2404.01459/) (2024)
- [First Field-Trial Demonstration of L4 Autonomous Optical Network for Distributed AI Training Communication: An LLM-Powered Multi-AI-Agent Solution](https://mlsanity.com/paper/2504.01234/) (2025)

## Abstract (arXiv)

Cloud workloads today are typically managed in a distributed environment and processed across geographically distributed data centers. Cloud service providers have been distributing data centers globally to reduce operating costs while also improving quality of service by using intelligent workload and resource management strategies. Such large scale and complex orchestration of software workload and hardware resources remains a difficult problem to solve efficiently. Researchers and practitioners have been trying to address this problem by proposing a variety of cloud management techniques. Mathematical optimization techniques have historically been used to address cloud management issues. But these techniques are difficult to scale to geo-distributed problem sizes and have limited applicability in dynamic heterogeneous system environments, forcing cloud service providers to explore intelligent data-driven and Machine Learning (ML) based alternatives. The characterization, prediction, control, and optimization of complex, heterogeneous, and ever-changing distributed cloud resources and workloads employing ML methodologies have received much attention in recent years. In this article, we review the state-of-the-art ML techniques for the cloud data center management problem. We examine the challenges and the issues in current research focused on ML for cloud management and explore strategies for addressing these issues. We also discuss advantages and disadvantages of ML techniques presented in the recent literature and make recommendations for future research directions.