AI Driven Operating System Resource Manager

Abstract

In today\'s complex computing systems, there is a need for adaptive and intelligent resource management within operating systems. Existing operating systems use static scheduling policies and fixed heuristics that often lack dynamic responsiveness to fluctuating system states and dynamic workloads. This work proposes an AI-Driven Intelligent Operating System Optimization Framework that enables real-time, data-driven optimization. This system uses system-level logs, feature engineering and machine learning models to analyze and predict resource usage trends like CPU utilization, memory usage and process behaviors. It contains a decision engine that takes autonomous actions on optimizing resources by adapting process prioritization, dynamic CPU allocation and memory optimization. This system also includes a simulator for comparing static operating systems to AI-driven OS performance. The framework is also equipped with a web-based dashboard for real-time monitoring and visualization. This system provides a closed-loop learning environment with the machine learning models and decision engine learning from past and present data and improving their predictive capabilities and optimization decision- making power over time. Both simulation and real-time analysis results indicate system-wide improvements in responsiveness and resource utilization and efficiency.

Introduction

The text presents an AI-based operating system optimization framework designed to overcome the limitations of traditional operating systems that rely on static scheduling and fixed rules. As modern computing environments become more dynamic and complex, conventional systems struggle with inefficient resource allocation, leading to reduced performance, delays, and energy waste.

The proposed system introduces an intelligent, data-driven approach that continuously monitors system performance (CPU, memory, and processes), learns from historical and real-time data, and dynamically optimizes resource allocation using machine learning. It operates as a closed-loop system with key modules: data collection, feature engineering, machine learning, decision engine, visualization, and feedback. These modules work together to predict system states, detect issues like overload, and automatically adjust resources such as CPU priority and memory usage.

The framework also includes a simulation layer to compare its performance with traditional operating systems, demonstrating improvements in efficiency and responsiveness. The system follows a continuous cycle of data collection, processing, prediction, decision-making, and feedback to enhance performance over time.

Core principles of the system include adaptability, automation, real-time responsiveness, scalability, and data-driven decision-making. Key innovations include integrating machine learning directly into the OS, predictive optimization, continuous learning from system logs, and automated decision execution.

Overall, the proposed system aims to create a smarter, self-adaptive operating system that improves performance, reduces human intervention, efficiently manages resources, and lays the foundation for next-generation intelligent computing environments.

Conclusion

In this paper, a simulation-based validation of an AI-based operating system optimization framework has been presented. This framework utilizes intelligent resource management in order to maximize system performance. It encompasses a system for collecting system data and its respective feature engineering along with a module which utilizes a machine learning algorithm to make a decision. Data is continuously provided and processed in order to form an optimizing loop driven by feedback. Through utilization of a learning based machine learning algorithm, the system analyzes the behavior of the operating system, forecasts the workload conditions and implements an proactive approach for optimization which is responsible for increasing CPU performance , minimizing system lagging, and memory utilization. Moreover, through utilization of an automated decision engine, the optimization process is autonomous, rendering higher system efficiency and reliability. The inclusion of the simulation framework adds more value to this research work by providing an experimentation to validate the system. With simulation the comparison of performance of the AI-driven system to that of traditional static OS is made under controlled circumstances which reveals improvement of system metrics like utilization and latency of CPU and memory. In the future this framework may lead to a futu re generation OS which will be adaptive, intelligent and learning-based. Further enhancements to the system will involve use of a deeper learning algorithm for making prediction, integrating this framework with cloud based operating systems and kernel lev el optimization in real time

References

[1] Shankar, V.; Srivatsava, K.B.; Li, X. Enhancing Operating System Performance with AI: Optimized Scheduling and Resource Management. International Journal of Artificial Intelligence & Machine Learning (IJAIML), 2025, 4(1), 172–191. [2] Wang, Y.; Xing, S. AI-Driven CPU Resource Management in Cloud Operating Systems. Journal of Computer and Communications, 2025, 13, 135–149. [3] Korshun, N. The Role of Artificial Intelligence and Machine Learning in Operating System Management. In Proceedings of CEUR Workshop Proceedings, 2023. [4] Akgun, I.U. Using Machine Learning to Improve Operating Systems’ I/O Performance. Stony Brook University Technical Report, 2022. [5] Safarzadeh, V.M.; Loghmani, H.G. Artificial Intelligence in the Low-Level Realm—A Survey. arXiv preprint, 2021. [6] Coppock, P.H.; Zhang, B.; Solomon, E.H.; Kypriotis, V.; Yang, L.; Sharma, B.; Schatzberg, D.; Mowry, T.C.; Skarlatos, D. LithOS: An Operating System for Efficient Machine Learning on GPUs. arXiv preprint, 2025. [7] Bitchebe, S.; Balmau, O. MaLV-OS: Rethinking the Operating System Architecture for Machine Learning in Virtualized Clouds. arXiv preprint, 2025. [8] Singh, R.; Kothari, V. Composable OS Kernel Architectures for Autonomous Intelligence. arXiv preprint, 2025. [9] Mao, H.; Alizadeh, M.; Menache, I.; Kandula, S. Resource Management with Deep Reinforcement Learning. In Proceedings of the 15th ACM Workshop on Hot Topics in Networks (HotNets), Atlanta, GA, USA, 9–10 November 2016; pp. 50–56. [10] Xu, J.; Chen, L.; Wang, P. Dynamic Resource Allocation Using Machine Learning in Cloud Computing Environment. Future Generation Computer Systems, 2020, 108, 610–620. [11] Ghodsi, A.; Zaharia, M.; Hindman, B.; Konwinski, A.; Shenker, S.; Stoica, I. Dominant Resource Fairness: Fair Allocation of Multiple Resource Types. In Proceedings of the 8th USENIX Symposium on Networked Systems Design and Implementation (NSDI), Boston, MA, USA, 2011; pp. 323–336. [12] Verma, A.; Ahuja, P.; Neogi, A. Power-Aware Dynamic Placement of HPC Applications. In Proceedings of the 22nd ACM International Conference on Supercomputing (ICS), Island of Kos, Greece, 2008; pp. 175–184. [13] Tesauro, G.; Jong, N.K.; Das, R.; Bennani, M.N. A Hybrid Reinforcement Learning Approach to Autonomic Resource Allocation. In Proceedings of the IEEE International Conference on Autonomic Computing (ICAC), Dublin, Ireland, 2006; pp. 65–73. [14] Zhang, Q.; Chen, M.; Li, L. Machine Learning-Based Workload Prediction in Cloud Computing. IEEE Transactions on Cloud Computing, 2019, 7(1), 217–230. [15] Caron, E.; Desprez, F.; Loureiro, D. Cloud Computing Resource Management Through a Grid Middleware: A Survey. Journal of Grid Computing, 2010, 8(3), 397–416. [16] Mishra, A.; Sahoo, B.; Parida, P.P. Load Balancing in Cloud Computing: A Big Picture. Journal of King Saud University – Computer and Information Sciences, 2020, 32(2), 149–158. [17] Delimitrou, C.; Kozyrakis, C. Quasar: Resource-Efficient and QoS-Aware Cluster Management. In Proceedings of the 19th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), Salt Lake City, UT, USA, 2014; pp. 127–144. [18] Grandl, R.; Ananthanarayanan, G.; Kandula, S.; Rao, S. Multi-Resource Packing for Cluster Schedulers. In Proceedings of the ACM SIGCOMM Conference, Chicago, IL, USA, 2014; pp. 455–466.

Copyright

Copyright © 2026 Karishma Karande, Krish Chavhan, Meghna Kolhe, Kunal Kuranjekar, Om Yelane, Manthan Sarve, Krutika Thakre. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.