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

2024-07-19

AI Summary

This paper addresses long-standing bottlenecks in traditional operating systems—namely, scalability, adaptability, and manageability—by proposing a systematic framework for deep AI–OS integration. Methodologically, it introduces the first three-stage AI–OS co-evolution path: AI-powered → AI-refactored → AI-driven; designs verifiable kernel-level inference mechanisms, hybrid rule- and AI-based decision models, and modular AI-ready kernel interfaces; and spans the full software stack (kernel, drivers, runtime, toolchain), integrating machine learning, large language models, and agent-based techniques—with real-time constraint modeling, dynamic workload forecasting, and edge-coordinated inference. Contributions include: (1) a unified analytical framework for AI–OS interaction; (2) standardized evaluation dimensions and a methodology pipeline; and (3) a production-oriented integration blueprint and benchmarking recommendations—collectively providing both theoretical foundations and actionable engineering pathways for AI–OS co-evolution.

Technology Category

• Multiagent Systems: Agent/AI Theories and Architectures
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Machine Learning: Deep Neural Architectures and Foundation Models

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Addressing OS bottlenecks in scalability, adaptability, and manageability
• Integrating AI techniques like ML and LLMs into operating systems
• Developing AI-enhanced systems to replace heuristic-based OS designs

Innovation

• AI-enhanced systems replacing heuristic-based designs
• Modular kernel interfaces enabling AI integration
• Hybrid rules-plus-AI decisions with safety guardrails