用双DQN动态调优先级，提升系统任务调度效率。
Dynamic Operating System Scheduling Using Double DQN: A Reinforcement Learning Approach to Task Optimization
- 用双DQN模型实时学习并调整任务优先级与资源分配。
- 在轻、中、重负载下均显著缩短任务完成与响应时间。
- 适合云环境和分布式系统中的智能调度优化场景。
本文提出一种基于双DQN（Double Deep Q Network）的操作系统调度算法，并通过实验验证其在不同任务类型和系统负载下的性能。相比传统调度算法，该方法可动态调整任务优先级与资源分配策略，从而提升任务完成效率、系统吞吐量和响应速度。实验结果表明，双DQN算法在轻载、中载和重载场景下均表现出色，尤其在处理I/O密集型任务时效果显著，能有效降低任务完成时间和系统响应时间。此外，该算法在资源利用率方面也展现出强优化能力，可根据系统状态智能调整资源分配，避免资源浪费与过载。未来研究将探索其在云计算和大规模分布式环境中的应用，结合网络延迟与能效等因子，进一步提升算法的整体性能与适应性。
原文摘要 · Abstract (English)
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.
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