# Deep Reinforcement Learning for Job Scheduling and Resource Management in Cloud Computing: An Algorithm-Level Review

arXiv: https://arxiv.org/abs/2501.01007 · HTML: https://mlsanity.com/paper/2501.01007/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 27 of its 212 references that are in the map.
- First posted on arXiv 2 Jan 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 27 of 27)

- [Deep Reinforcement Learning: An Overview](https://mlsanity.com/paper/1701.07274/) (2017)
- ["Other-Play" for Zero-Shot Coordination](https://mlsanity.com/paper/2003.02979/) (2020)
- [Exploration in Deep Reinforcement Learning: A Survey](https://mlsanity.com/paper/2205.00824/) (2022)
- [Independent Policy Gradient Methods for Competitive Reinforcement Learning](https://mlsanity.com/paper/2101.04233/) (2021)
- [Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity](https://mlsanity.com/paper/2007.07461/) (2020)
- [Resource Scheduling in Edge Computing: A Survey](https://mlsanity.com/paper/2108.08059/) (2021)
- [A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning](https://mlsanity.com/paper/1703.04221/) (2017)
- [Dynamic Scheduling for Stochastic Edge-Cloud Computing Environments using A3C learning and Residual Recurrent Neural Networks](https://mlsanity.com/paper/2009.02186/) (2020)
- [Deep Reinforcement Learning-based Methods for Resource Scheduling in Cloud Computing: A Review and Future Directions](https://mlsanity.com/paper/2105.04086/) (2021)
- [Multi-agent Reinforcement Learning for Resource Allocation in IoT networks with Edge Computing](https://mlsanity.com/paper/2004.02315/) (2020)
- [PantheonRL: A MARL Library for Dynamic Training Interactions](https://mlsanity.com/paper/2112.07013/) (2021)
- [GA-DRL: Graph Neural Network-Augmented Deep Reinforcement Learning for DAG Task Scheduling over Dynamic Vehicular Clouds](https://mlsanity.com/paper/2307.00777/) (2023)
- [DL-DRL: A double-level deep reinforcement learning approach for large-scale task scheduling of multi-UAV](https://mlsanity.com/paper/2208.02447/) (2022)
- [IMP-MARL: a Suite of Environments for Large-scale Infrastructure Management Planning via MARL](https://mlsanity.com/paper/2306.11551/) (2023)
- [Stacked Auto Encoder Based Deep Reinforcement Learning for Online Resource Scheduling in Large-Scale MEC Networks](https://mlsanity.com/paper/2001.09223/) (2020)
- [Resource Allocation and Service Provisioning in Multi-Agent Cloud Robotics: A Comprehensive Survey](https://mlsanity.com/paper/2104.14270/) (2021)
- [Task Placement and Resource Allocation for Edge Machine Learning: A GNN-based Multi-Agent Reinforcement Learning Paradigm](https://mlsanity.com/paper/2302.00571/) (2023)
- [RCsearcher: Reaction Center Identification in Retrosynthesis via Deep Q-Learning](https://mlsanity.com/paper/2301.12071/) (2023)
- [Deep Reinforcement Learning-based Scheduling for Optimizing System Load and Response Time in Edge and Fog Computing Environments](https://mlsanity.com/paper/2309.07407/) (2023)
- [TF-DDRL: A Transformer-enhanced Distributed DRL Technique for Scheduling IoT Applications in Edge and Cloud Computing Environments](https://mlsanity.com/paper/2410.14348/) (2024)
- [Reinforcement Learning-driven Data-intensive Workflow Scheduling for Volunteer Edge-Cloud](https://mlsanity.com/paper/2407.01428/) (2024)
- [Digital Twin-Empowered Network Planning for Multi-Tier Computing](https://mlsanity.com/paper/2210.02616/) (2022)
- [A Deep Reinforcement Learning based Algorithm for Time and Cost Optimized Scaling of Serverless Applications](https://mlsanity.com/paper/2308.11209/) (2023)
- [Differentiate Quality of Experience Scheduling for Deep Learning Inferences with Docker Containers in the Cloud](https://mlsanity.com/paper/2010.12728/) (2020)
- [CILP: Co-simulation based Imitation Learner for Dynamic Resource Provisioning in Cloud Computing Environments](https://mlsanity.com/paper/2302.05630/) (2023)
- [Incremental DRL-Based Resource Management for Dynamic Network Slicing in an Urban-Wide Testbed](https://mlsanity.com/paper/2310.17523/) (2023)
- [Generative AI-enabled Quantum Computing Networks and Intelligent Resource Allocation](https://mlsanity.com/paper/2401.07120/) (2024)

## Built on by (most cited first, 4 of 4)

- [SLA-MORL: SLA-Aware Multi-Objective Reinforcement Learning for HPC Resource Optimization](https://mlsanity.com/paper/2508.03509/) (2025)
- [ReLATE: Accelerating Tensor Decomposition via Safe and Efficient Learning of Sparse Encodings](https://mlsanity.com/paper/2509.00280/) (2025)
- [Intelligent Cloud Orchestration: A Hybrid Predictive and Heuristic Framework for Cost Optimization](https://mlsanity.com/paper/2604.02131/) (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)

Cloud computing has revolutionized the provisioning of computing resources, offering scalable, flexible, and on-demand services to meet the diverse requirements of modern applications. At the heart of efficient cloud operations are job scheduling and resource management, which are critical for optimizing system performance and ensuring timely and cost-effective service delivery. However, the dynamic and heterogeneous nature of cloud environments presents significant challenges for these tasks, as workloads and resource availability can fluctuate unpredictably. Traditional approaches, including heuristic and meta-heuristic algorithms, often struggle to adapt to these real-time changes due to their reliance on static models or predefined rules. Deep Reinforcement Learning (DRL) has emerged as a promising solution to these challenges by enabling systems to learn and adapt policies based on continuous observations of the environment, facilitating intelligent and responsive decision-making. This survey provides a comprehensive review of DRL-based algorithms for job scheduling and resource management in cloud computing, analyzing their methodologies, performance metrics, and practical applications. We also highlight emerging trends and future research directions, offering valuable insights into leveraging DRL to advance both job scheduling and resource management in cloud computing.