Integrating Artificial Intelligence into Operating Systems: A Survey on Techniques, Applications, and Future Directions

Understand

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.