mlsanityData to 14 Oct 2026

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

arXiv 2501.01007 ↗First posted 2 Jan 2025cs.DC cs.AIcited by 4

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

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.

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.

Builds on

27 of 212 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Reinforcement Learning-driven Data-intensive Workflow Scheduling for Volunteer Edge-Cloud1 Jul

Other references · 26

  1. 2017Deep Reinforcement Learning: An Overviewcited by 333
  2. 2020"Other-Play" for Zero-Shot Coordinationcited by 198
  3. 2022Exploration in Deep Reinforcement Learning: A Surveycited by 166
  4. 2021Independent Policy Gradient Methods for Competitive Reinforcement Learningcited by 101
  5. 2020Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexitycited by 92
  6. 2021Resource Scheduling in Edge Computing: A Surveycited by 17
  7. 2017A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learningcited by 16
  8. 2020Dynamic Scheduling for Stochastic Edge-Cloud Computing Environments using A3C learning and Residual Recurrent Neural Networkscited by 14
  9. 2021Deep Reinforcement Learning-based Methods for Resource Scheduling in Cloud Computing: A Review and Future Directionscited by 14
  10. 2020Multi-agent Reinforcement Learning for Resource Allocation in IoT networks with Edge Computingcited by 13
  11. 2021PantheonRL: A MARL Library for Dynamic Training Interactionscited by 10
  12. 2023GA-DRL: Graph Neural Network-Augmented Deep Reinforcement Learning for DAG Task Scheduling over Dynamic Vehicular Cloudscited by 10
14 more
  1. 2022DL-DRL: A double-level deep reinforcement learning approach for large-scale task scheduling of multi-UAVcited by 9
  2. 2023IMP-MARL: a Suite of Environments for Large-scale Infrastructure Management Planning via MARLcited by 9
  3. 2020Stacked Auto Encoder Based Deep Reinforcement Learning for Online Resource Scheduling in Large-Scale MEC Networkscited by 7
  4. 2021Resource Allocation and Service Provisioning in Multi-Agent Cloud Robotics: A Comprehensive Surveycited by 7
  5. 2023Task Placement and Resource Allocation for Edge Machine Learning: A GNN-based Multi-Agent Reinforcement Learning Paradigmcited by 7
  6. 2023RCsearcher: Reaction Center Identification in Retrosynthesis via Deep Q-Learningcited by 6
  7. 2023Deep Reinforcement Learning-based Scheduling for Optimizing System Load and Response Time in Edge and Fog Computing Environmentscited by 6
  8. 2024TF-DDRL: A Transformer-enhanced Distributed DRL Technique for Scheduling IoT Applications in Edge and Cloud Computing Environmentscited by 6
  9. 2022Digital Twin-Empowered Network Planning for Multi-Tier Computingcited by 4
  10. 2023A Deep Reinforcement Learning based Algorithm for Time and Cost Optimized Scaling of Serverless Applicationscited by 3
  11. 2020Differentiate Quality of Experience Scheduling for Deep Learning Inferences with Docker Containers in the Cloudcited by 2
  12. 2023CILP: Co-simulation based Imitation Learner for Dynamic Resource Provisioning in Cloud Computing Environmentscited by 1
  13. 2023Incremental DRL-Based Resource Management for Dynamic Network Slicing in an Urban-Wide Testbedcited by 1
  14. 2024Generative AI-enabled Quantum Computing Networks and Intelligent Resource Allocationcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025SLA-MORL: SLA-Aware Multi-Objective Reinforcement Learning for HPC Resource Optimizationcited by 0
  2. 2025ReLATE: Accelerating Tensor Decomposition via Safe and Efficient Learning of Sparse Encodingscited by 0
  3. 2026Intelligent Cloud Orchestration: A Hybrid Predictive and Heuristic Framework for Cost Optimizationcited by 0
  4. 2026MCRL2: Multi-resource Cross-attention-based Representation Learning-augmented Reinforcement Learning for Cloud Microservice Schedulingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Research on Edge Computing and Cloud Collaborative Resource Scheduling Optimization Based on Deep Reinforcement Learning
  2. 2024An Advanced Reinforcement Learning Framework for Online Scheduling of Deferrable Workloads in Cloud Computing
  3. 2025Intelligent Resource Allocation Optimization for Cloud Computing via Machine Learning
  4. 2026Deft Scheduling of Dynamic Cloud Workflows with Varying Deadlines via Mixture-of-Experts
  5. 2025Machine learning-based cloud resource allocation algorithms: a comprehensive comparative review
  6. 2026On the Role of DAG topology in Energy-Aware Cloud Scheduling : A GNN-Based Deep Reinforcement Learning Approach
  7. 2025GATES: Cost-aware Dynamic Workflow Scheduling via Graph Attention Networks and Evolution Strategy
  8. 2024Reinforcement Learning-Based Adaptive Load Balancing for Dynamic Cloud Environments