# Deep Reinforcement Learning-based Methods for Resource Scheduling in Cloud Computing: A Review and Future Directions

arXiv: https://arxiv.org/abs/2105.04086 · HTML: https://mlsanity.com/paper/2105.04086/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2021.

## Built on by (most cited first, 14 of 14)

- [Multi-Agent Reinforcement Learning for Adaptive Resource Orchestration in Cloud-Native Clusters](https://mlsanity.com/paper/2508.10253/) (2025)
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- [Deep Reinforcement Learning for Job Scheduling and Resource Management in Cloud Computing: An Algorithm-Level Review](https://mlsanity.com/paper/2501.01007/) (2025)
- [A Survey on Machine Learning for Geo-Distributed Cloud Data Center Management](https://mlsanity.com/paper/2205.08072/) (2022)
- [Gazing at Rewards: Eye Movements as a Lens into Human and AI Decision-Making in Hybrid Visual Foraging](https://mlsanity.com/paper/2411.09176/) (2024)
- [Job Scheduling in Datacenters using Constraint Controlled RL](https://mlsanity.com/paper/2211.05338/) (2022)
- [Cluster Workload Allocation: A Predictive Approach Leveraging Machine Learning Efficiency](https://mlsanity.com/paper/2509.17695/) (2025)
- [An Advanced Reinforcement Learning Framework for Online Scheduling of Deferrable Workloads in Cloud Computing](https://mlsanity.com/paper/2406.01047/) (2024)
- [Symmetry-Preserving Architecture for Multi-NUMA Environments (SPANE): A Deep Reinforcement Learning Approach for Dynamic VM Scheduling](https://mlsanity.com/paper/2504.14946/) (2025)
- [QAISim: A Toolkit for Modeling and Simulation of AI in Quantum Cloud Computing Environments](https://mlsanity.com/paper/2512.17918/) (2025)
- [Cluster Workload Allocation: Semantic Soft Affinity Using Natural Language Processing](https://mlsanity.com/paper/2601.09282/) (2026)
- [Agentic-Kube: A Graph-Enhanced Multi-Agent Reinforcement Learning Framework for Multi-Objective Kubernetes Scheduling](https://mlsanity.com/paper/2603.12031/) (2026)
- [Deft Scheduling of Dynamic Cloud Workflows with Varying Deadlines via Mixture-of-Experts](https://mlsanity.com/paper/2606.01162/) (2026)
- [MCRL2: Multi-resource Cross-attention-based Representation Learning-augmented Reinforcement Learning for Cloud Microservice Scheduling](https://mlsanity.com/paper/2609.13048/) (2026)

## Abstract (arXiv)

As the quantity and complexity of information processed by software systems increase, large-scale software systems have an increasing requirement for high-performance distributed computing systems. With the acceleration of the Internet in Web 2.0, Cloud computing as a paradigm to provide dynamic, uncertain and elastic services has shown superiorities to meet the computing needs dynamically. Without an appropriate scheduling approach, extensive Cloud computing may cause high energy consumptions and high cost, in addition that high energy consumption will cause massive carbon dioxide emissions. Moreover, inappropriate scheduling will reduce the service life of physical devices as well as increase response time to users' request. Hence, efficient scheduling of resource or optimal allocation of request, that usually a NP-hard problem, is one of the prominent issues in emerging trends of Cloud computing. Focusing on improving quality of service (QoS), reducing cost and abating contamination, researchers have conducted extensive work on resource scheduling problems of Cloud computing over years. Nevertheless, growing complexity of Cloud computing, that the super-massive distributed system, is limiting the application of scheduling approaches. Machine learning, a utility method to tackle problems in complex scenes, is used to resolve the resource scheduling of Cloud computing as an innovative idea in recent years. Deep reinforcement learning (DRL), a combination of deep learning (DL) and reinforcement learning (RL), is one branch of the machine learning and has a considerable prospect in resource scheduling of Cloud computing. This paper surveys the methods of resource scheduling with focus on DRL-based scheduling approaches in Cloud computing, also reviews the application of DRL as well as discusses challenges and future directions of DRL in scheduling of Cloud computing.