Impact of AI in the Design of Operating System: An Overview

Authors

DOI:

https://doi.org/10.48001/978-81-980647-6-9-7

Keywords:

Operating system, Automation, User Interface, Security

Abstract

The rapid advancement of Artificial Intelligence (AI) has significantly influenced the design and functionality of modern operating systems (OS). This paper explores the integration of AI techniques, such as machine learning, neural networks, and reinforcement learning, into various OS components, including resource management, security, energy efficiency, and user interaction. AI-driven operating systems enhance performance through intelligent scheduling, dynamic resource allocation, and adaptive energy management, while also improving system security by detecting and mitigating threats in real-time. Furthermore, AI contributes to automated system maintenance, self-healing capabilities, and personalized user experiences. This paper highlights the transformative role of AI in making operating systems more autonomous, efficient, and user-centric, providing insights into current applications, challenges, and future directions.

Downloads

References

Akgun, I., Aydin, A. S., Burford, A., McNeill, M., Arkhangelskiy, M., & Zadok, E. (2022). Improving storage systems using machine learning. ACM Transactions on Storage, 19(1), 1–30. https://doi.org/10.1145/3568429

Doudali, T. D., & Gavrilovska, A. (2021). Machine learning augmented hybrid memory management. High Performance Distributed Computing, 253–254. https://doi.org/10.1145/3431379.3464450

Ge, S. (2024). Research on intelligent file management system: A design strategy based on RFID technology and improved AICT algorithm. PeerJ Computer Science. https://doi.org/10.7717/peerj-cs.1794

Kato, S., Rajkumar, R., & Ishikawa, Y. (2011). CPU scheduling and memory management for interactive real-time applications. Real-Time Systems, 47(5), 454–488. https://doi.org/10.1007/s11241-011-9127-8

Kumar, K. N. (2024). Open-AI model efficient memory reduce management for the large language models (LLMs) serving with paged attention of sharing the KV caches. International Journal for Science Technology and Engineering, 12(8), 688–693. https://doi.org/10.22214/ijraset.2024.63985

Maas, M., Andersen, D. G., Isard, M., Javanmard, M. M., McKinley, K. S., & Raffel, C. (2020). Learning-based memory allocation for C++ server workloads. Architectural Support for Programming Languages and Operating Systems, 541–556. https://doi.org/10.1145/3373376.3378525

Moni, M. M. A., Khan, F. J., Juboraj, M. F.-U.-A., Chakrabarty, A., Niloy, M., & Chowdhury, A. H. (2022). Comparative analysis of process scheduling algorithm using AI models. 2022 25th International Conference on Computer and Information Technology (ICCIT), 587–592. https://doi.org/10.1109/iccit57492.2022.10055395

Olekar, V., Patil, Y., Pawar, V., Pitale, S., Jaiswal, V. R., & Naranje, V. (2024). Survey on use of AI/ML algorithms in enhancing filesystem performance and efficiency. International Conference on Recent Innovations in Technology and Engineering (ICRITO), 1–4. https://doi.org/10.1109/icrito61523.2024.10522205

Park, J., Jo, C., Lee, Y., Park, B., & Lee, S. (2020). Performance-efficient CPU resource management algorithm on heterogeneous multi-processor. 2020 IEEE International Conference on Consumer Electronics (ICCE), 1–5. https://doi.org/10.1109/icce46568.2020.9043140

Ponnusamy, V., & Vasuki, A. (2022). AI-enabled intelligent resource management in 6G. Springer Nature Singapore. https://doi.org/10.1007/978-981-19-2868-0_4

Selman, A. H., Selman, S., & Aburas, A. (2014). Intelligent memory allocation based on fuzzy logic. Southeast Europe Journal of Soft Computing, 3(1). https://doi.org/10.21533/scjournal.v3i1.14

Selvarajan, G. P. (2024). AI-driven cloud resource management and orchestration. International Journal of Innovative Research in Science, Engineering and Technology, 13(11), 19367–19380. https://doi.org/10.15680/ijirset.2024.1311206

Seo, J., Kim, W.-H., Noh, S. H., Baek, W., & Nam, B. (2017). Failure-atomic slotted paging for persistent memory. Proceedings of Architectural Support for Programming Languages and Operating Systems (ASPLOS’17), 52(4), 91–104. https://doi.org/10.1145/3037697.3037737

Voss, M., Asenjo, R., & Reinders, J. (2019). Scalable memory allocation. Apress. https://doi.org/10.1007/978-1-4842-4398-5_7

Wang, J., & Tropper, C. (2007). Optimizing time warp simulation with reinforcement learning techniques. Simulation Conference, 2007 Winter, 2, 577–584. https://doi.org/10.1109/wsc.2007.4419650

Yang, C., Wang, L., Zhang, H., Lin, F., Jiang, W., & Zhang, J. (2023). Combining forecasting and multi-agent reinforcement learning techniques on power grid scheduling task. 2023 IEEE 2nd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA), 1576–1580. https://doi.org/10.1109/eebda56825.2023.10090669

Zhang, H., Zhao, F., Huang, L., Ouyang, J., Li, M., Hong, J., Gu, W., & Xu, H. (2019). File intelligent management system.

Downloads

Published

2025-03-17

How to Cite

Pandikumar, S., Jakaraddi, H. R., & Sevugapandi, N. (2025). Impact of AI in the Design of Operating System: An Overview. QTanalytics Publication (Books), 64–73. https://doi.org/10.48001/978-81-980647-6-9-7

Most read articles by the same author(s)