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Dynamic Operating System Scheduling Using Double DQN: A Reinforcement Learning Approach to Task Optimization

arXiv 2503.23659 ↗First posted 31 Mar 2025cs.LGcited by 10

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Abstract (arXiv)

In this paper, an operating system scheduling algorithm based on Double DQN (Double Deep Q network) is proposed, and its performance under different task types and system loads is verified by experiments. Compared with the traditional scheduling algorithm, the algorithm based on Double DQN can dynamically adjust the task priority and resource allocation strategy, thus improving the task completion efficiency, system throughput, and response speed. The experimental results show that the Double DQN algorithm has high scheduling performance under light load, medium load and heavy load scenarios, especially when dealing with I/O intensive tasks, and can effectively reduce task completion time and system response time. In addition, the algorithm also shows high optimization ability in resource utilization and can intelligently adjust resource allocation according to the system state, avoiding resource waste and excessive load. Future studies will further explore the application of the algorithm in more complex systems, especially scheduling optimization in cloud computing and large-scale distributed environments, combining factors such as network latency and energy efficiency to improve the overall performance and adaptability of the algorithm.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 6 of its 19 references that are in the map. First posted on arXiv 31 Mar 2025.

Builds on

6 of 19 references.

References · 6

  1. 2025Contrastive Learning for Cold Start Recommendation with Adaptive Feature Fusioncited by 11
  2. 2025Object Detection for Medical Image Analysis: Insights from the RT-DETR Modelcited by 10
  3. 2025A Deep Learning Framework Integrating CNN and BiLSTM for Financial Systemic Risk Analysis and Predictioncited by 9
  4. 2025A Structured Reasoning Framework for Unbalanced Data Classification Using Probabilistic Modelscited by 7
  5. 2025Multi-Scale Transformer Architecture for Accurate Medical Image Classificationcited by 5
  6. 2025Dynamic Adaptation of LoRA Fine-Tuning for Efficient and Task-Specific Optimization of Large Language Modelscited by 4

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Citing papers per year: 2025: 10.

10 papers.

  1. 2025Multi-Agent Reinforcement Learning for Adaptive Resource Orchestration in Cloud-Native Clusterscited by 12
  2. 2025Structure-Aware Temporal Modeling for Chronic Disease Progression Predictioncited by 7
  3. 2025Deep Probabilistic Modeling of User Behavior for Anomaly Detection via Mixture Density Networkscited by 6
  4. 2025Artificial Intelligence-Based Multiscale Temporal Modeling for Anomaly Detection in Cloud Servicescited by 6
  5. 2025Knowledge Graph-Infused Fine-Tuning for Structured Reasoning in Large Language Modelscited by 5
  6. 2025Graph Neural Network-Based Collaborative Perception for Adaptive Scheduling in Distributed Systemscited by 4
  7. 2025Time-Series Learning for Proactive Fault Prediction in Distributed Systems with Deep Neural Structurescited by 4
  8. 2025Federated Anomaly Detection for Multi-Tenant Cloud Platforms with Personalized Modelingcited by 1
  9. 2025Multi-Level Service Performance Forecasting via Spatiotemporal Graph Neural Networkscited by 0
  10. 2025Mixture-of-Schedulers: An Adaptive Scheduling Agent as a Learned Router for Expert Policiescited by 0

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  3. 2025Intelligent Resource Allocation Optimization for Cloud Computing via Machine Learning
  4. 2025Deep Reinforcement Learning for Job Scheduling and Resource Management in Cloud Computing: An Algorithm-Level Review
  5. 2025Research on Edge Computing and Cloud Collaborative Resource Scheduling Optimization Based on Deep Reinforcement Learning
  6. 2024Towards Fair and Firm Real-Time Scheduling in DNN Multi-Tenant Multi-Accelerator Systems via Reinforcement Learning
  7. 2025GATES: Cost-aware Dynamic Workflow Scheduling via Graph Attention Networks and Evolution Strategy
  8. 2024Cost-Aware Dynamic Cloud Workflow Scheduling using Self-Attention and Evolutionary Reinforcement Learning