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

2025-03-31 · Xiaoxuan Sun, Yifei Duan, Yingnan Deng, Fan Guo, Guohui Cai, Yuting Peng

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.

📄 PDF Abstract BibTeX arXiv:2503.23659

Code (0)

등록된 구현이 없습니다.

Tasks

Cloud ComputingScheduling

Methods 이 논문이 사용한 방법론

Experience Replay Experience Replay is a replay memory technique used in reinforcement learning where we store the agent’s experiences at each time-step, $e\_{t} = \left(s\_{t}, a\_{t}, r\_{t},…

Q-Learning Q-Learning is an off-policy temporal difference control algorithm: $$Q\left(S\_{t}, A\_{t}\right) \leftarrow Q\left(S\_{t}, A\_{t}\right) + \alpha\left[R_{t+1} +…

Double Q-learning Double Q-learning is an off-policy reinforcement learning algorithm that utilises double estimation to counteract overestimation problems with traditional Q-learning. The…

Convolution A convolution is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the…

Dense Connections Dense Connections, or Fully Connected Connections, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output…

Double DQN 설명 없음

DQN A DQN, or Deep Q-Network, approximates a state-value function in a Q-Learning framework with a neural network. In the Atari…

Deep reinforcement learning-based joint real-time energy scheduling for green buildings with heterogeneous battery energy storage devices

2025-06-07 · Chi Liu, Zhezhuang Xu, Jiawei Zhou, Yazhou Yuan 외

Green buildings (GBs) with renewable energy and building energy management systems (BEMS) enable efficient energy use and support sustainable development. Electric vehicles (EVs), as flexible storage resources, enhance s…

Deep Reinforcement Learning energy management Scheduling

Adaptive Double-Booking Strategy for Outpatient Scheduling Using Multi-Objective Reinforcement Learning

2026-03-07 · Ninda Nurseha Amalina, Heungjo An arxiv

Patient no-shows disrupt outpatient clinic operations, reduce productivity, and may delay necessary care. Clinics often adopt overbooking or double-booking to mitigate these effects. However, poorly calibrated policies c…

Reinforcement Learning

Reinforcement Learning for Adaptive Resource Scheduling in Complex System Environments

2024-11-08 · Pochun Li, Yuyang Xiao, Jinghua Yan, Xuan Li 외

This study presents a novel computer system performance optimization and adaptive workload management scheduling algorithm based on Q-learning. In modern computing environments, characterized by increasing data volumes, …

Cloud Computing Edge-computing Q-Learning reinforcement-learning+2

A Negotiation-Based Multi-Agent Reinforcement Learning Approach for Dynamic Scheduling of Reconfigurable Manufacturing Systems

2025-11-11 · Manonmani Sekar, Nasim Nezamoddini arxiv

Reconfigurable manufacturing systems (RMS) are critical for future market adjustment given their rapid adaptation to fluctuations in consumer demands, the introduction of new technological advances, and disruptions in li…

Multi-agent Reinforcement Learning

GA-DRL: Graph Neural Network-Augmented Deep Reinforcement Learning for DAG Task Scheduling over Dynamic Vehicular Clouds

2023-07-03 · Zhang Liu, Lianfen Huang, Zhibin Gao, Manman Luo 외

Vehicular clouds (VCs) are modern platforms for processing of computation-intensive tasks over vehicles. Such tasks are often represented as directed acyclic graphs (DAGs) consisting of interdependent vertices/subtasks a…

Deep Reinforcement Learning Graph Attention Graph Neural Network Scheduling