关键词: 操作系统, 机器学习, 进程调度, 内存管理,

Abstract: The rapid advancement of computer hardware and software applications poses significant challenges to modern operating systems (OS) in terms of efficient resource management. Increasing structural complexity and frequent updates in both software and hardware necessitate more intelligent and adaptive management strategies. Machine learning (ML) provides innovative solutions to address these issues. Existing research is examined to offer a systematic overview of ML-based methods for optimizing operating systems. Surveyed studies include ML-driven OS optimization techniques, concrete application cases of ML models across various OS contexts, and discussions of their strengths and limitations. Furthermore, ongoing challenges are identified, and promising future research directions are suggested, highlighting potential applications of machine learning in operating systems for enhanced resource management.

Key words: Operating System, Machine Learning, Process Scheduling, Memory Management;