将人工智能集成到操作系统中：技术、应用与未来方向综述
Integrating Artificial Intelligence into Operating Systems: A Survey on Techniques, Applications, and Future Directions
AI总结：
针对异构硬件和动态工作负载加剧的操作系统瓶颈，本综述统一分析人工智能与操作系统交叉领域的技术、应用等，总结双向进展与实践模式，指出挑战并提出三阶段路线图，助力人工智能在操作系统中可靠部署。
摘要：
异构硬件和动态工作负载加剧了操作系统在可扩展性、适应性和可管理性方面长期存在的瓶颈。同时，机器学习（ML）、大型语言模型（LLMs）和基于智能体的方法的进步实现了自动化和自优化，但当前的努力缺乏统一的视角。本综述回顾了人工智能与操作系统交叉领域的技术、架构、应用、挑战和未来方向。我们描绘了从启发式和基于规则的设计向人工智能增强系统的转变，概述了ML、LLMs和智能体在整个操作系统栈中的优势。我们总结了人工智能用于操作系统（核心组件和更广泛的生态系统）以及操作系统用于人工智能（组件级和架构级支持短上下文和长上下文推理、分布式训练和边缘推理）的进展。在实践方面，我们整合了平衡实时约束与预测准确性的评估维度、方法学管道和模式。我们识别了关键挑战，如复杂性、开销、模型漂移、有限的可解释性以及隐私和安全风险，并推荐了模块化的、人工智能就绪的内核接口；统一的工具链和基准；带有护栏的混合规则加人工智能决策；以及可验证的内核内推理。最后，我们提出了一个三阶段路线图，包括人工智能驱动、人工智能重构和人工智能主导的操作系统，以连接原型与生产，并实现可扩展、可靠的人工智能部署。
英文摘要：
Heterogeneous hardware and dynamic workloads worsen long-standing OS bottlenecks in scalability, adaptability, and manageability. At the same time, advances in machine learning (ML), large language models (LLMs), and agent-based methods enable automation and self-optimization, but current efforts lack a unifying view. This survey reviews techniques, architectures, applications, challenges, and future directions at the AI-OS intersection. We chart the shift from heuristic- and rule-based designs to AI-enhanced systems, outlining the strengths of ML, LLMs, and agents across the OS stack. We summarize progress in AI for OS (core components and the wider ecosystem) and in OS for AI (component- and architecture-level support for short- and long-context inference, distributed training, and edge inference). For practice, we consolidate evaluation dimensions, methodological pipelines, and patterns that balance real-time constraints with predictive accuracy. We identify key challenges, such as complexity, overhead, model drift, limited explainability, and privacy and safety risks, and recommend modular, AI-ready kernel interfaces; unified toolchains and benchmarks; hybrid rules-plus-AI decisions with guardrails; and verifiable in-kernel inference. Finally, we propose a three-stage roadmap including AI-powered, AI-refactored, and AI-driven OSs, to bridge prototypes and production and to enable scalable, reliable AI deployment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
