Operating System And Artificial Intelligence: A Systematic Review

Abstract: In the dynamic landscape of technology, the convergence of AI and Operating Systems (OS) has emerged as a pivotal arena for innovation. Our exploration focuses on the symbiotic relationship between AI and OS, emphasizing how AI-driven tools enhance OS performance, security, and efficiency, while OS advancements facilitate more sophisticated AI applications. We delve into various AI techniques employed to optimize OS functionalities, including memory management, process scheduling, and intrusion detection. Simultaneously, we analyze the role of OS in providing essential services and infrastructure that enable effective AI application execution, from resource allocation to data processing. The article also addresses challenges and future directions in this domain, emphasizing the imperative of secure and efficient AI integration within OS frameworks. By examining case studies and recent developments, our review provides a comprehensive overview of the current state of AI-OS integration, underscoring its significance in shaping the next generation of computing technologies. Finally, we explore the promising prospects of Intelligent OSes, considering not only how innovative OS architectures will pave the way for groundbreaking opportunities but also how AI will significantly contribute to advancing these next-generation OSs.

• The paper provides a comprehensive survey of AI techniques transforming OS architectures from static heuristics to adaptive, machine learning-based systems.
• It demonstrates how ML, reinforcement learning, and neural networks improve scheduling, I/O optimization, and security measures in operating systems.
• It outlines future directions and challenges for integrating AI into OS design, emphasizing performance overhead, model reliability, and privacy concerns.

Integrating Artificial Intelligence into Operating Systems

Introduction

The paper, "Integrating Artificial Intelligence into Operating Systems: A Survey on Techniques, Applications, and Future Directions" (2407.14567), comprehensively reviews the confluence of AI and operating systems (OS). It elucidates the transformation from static, heuristic-based OS designs to AI-enhanced systems, highlighting the paradigm shift towards automation and self-optimization. The survey traces how ML, LLMs, and agent-based intelligence are reshaping OS design, offering both opportunities and challenges.

Enhancing OS Capabilities

One significant theme is the application of AI to augment core OS capabilities:

• Process and Resource Management: The use of ML models, such as multi-layer perceptrons (MLP) within the Linux kernel, enhances scheduling by predicting load-balancing decisions with minimal latency. AI systems, including reinforcement learning (RL)-based schedulers, enable dynamic adaptation to workload variability, exceeding the scope of traditional static heuristics.
• I/O and Storage Optimization: AI-enhanced I/O systems leverage neural networks for predicting device behavior, achieving reduced latency and improved throughput. The use of ML in storage systems, as demonstrated by frameworks like LearnedFTL, optimizes flash translation layers by reducing double reads and thus enhancing performance.
• Security and Anomaly Detection: AI models, including autoencoders and deep belief networks, improve intrusion detection and malware detection accuracy. These enhancements shift OS security from reactive measures to proactive, data-driven strategies.

  Figure 1: Fundamental structure of an operating system. The diagram spans from hardware and device drivers at the base, through core kernel subsystems to system libraries, user interfaces, and applications.

OS as an AI Enabler

The paper also explores how OS advancements are supporting AI workloads:

• Memory and Execution Efficiency: AI workloads benefit from systems like Helix, which optimize data-loading and GPU utilization during distributed training. Similarly, memory management systems tailored for AI, like SDAM, provide adaptive address mapping to exploit high-bandwidth memory technologies efficiently.
• Distributed System Support: Systems such as Demikernel advocate for restructured OS architectures that bypass traditional paths, achieving significant latency reductions in datacenter-scale AI services.

  Figure 2: Workflow for the software-defined far memory system for warehouse-scale computers.

Developmental Roadmap

The research outlines a developmental roadmap for OS evolution:

• AI-Powered OS: This stage involves integrating intelligent modules into existing OS frameworks to enhance performance and usability.
• AI-Refactored OS: A deeper architectural transformation allows for modularity and more profound integration of AI-driven logic.
• AI-Driven OS: Looking forward, AI models will drive fundamental OS operations, enabling adaptive resource management.

Challenges and Future Directions

Despite promising advances, the paper identifies key challenges, including:

• System Complexity and Performance Overhead: AI integration increases OS complexity and may introduce significant overhead, which must be managed to avoid negating the benefits of automation.
• Model Drift and Reliability: The necessity of continually updating AI models to adapt to changing workloads presents challenges in maintaining stability and reliability.
• Privacy and Security: AI-augmented systems must address concerns regarding data privacy and security, particularly in sensitive or regulated environments.

  Figure 3: Workflow for enhancing operating systems with machine learning. In the training phase, data collected is preprocessed and used to train various models using advanced machine learning methods.

Conclusion

The integration of AI into operating systems marks a significant shift towards more intelligent, adaptive, and efficient resource management frameworks. By outlining the current landscape and future directions, this survey provides valuable insights into the transformative potential of AI in enhancing OS capabilities and supporting AI workloads. The path forward lies in addressing outstanding challenges, including model robustness, system complexity, and privacy, to realize the full potential of AI-driven operating systems.