Machine Learning for Linux Kernel Optimization: Current Trends and Future Directions
DOI:
https://doi.org/10.26438/ijcse/v13i3.5664Keywords:
Linux Kernel Optimization, Machine Learning in Operating Systems, Reinforcement Learning for CPU Scheduling, Memory Management using MLAbstract
The integration of Machine Learning into Linux Kernel optimization has revolutionized system performance by enabling dynamic resource allocation, adaptive scheduling, and intelligent power management. This paper explores current trends and future directions in machine learning driven kernel optimization, highlighting key applications such as reinforcement learning for CPU scheduling, predictive memory management, and ML-based congestion control in networking. We analyse the advantages of ML over traditional rule-based methods, demonstrating how data-driven optimization enhances efficiency and responsiveness. However, challenges such as interpretability, real-time constraints, and computational overhead pose significant barriers to widespread adoption. To address these, we discuss emerging solutions, including Explainable AI (XAI), federated learning for privacy-preserving model training, and AutoML for automated performance tuning. This study provides a comprehensive review of machine learning’s role in optimizing the Linux Kernel and outlines future research directions to maximize its potential in next-generation operating systems.
References
[1] H. Fingler, I. Tarte, H. Yu, A. Szekely, B. Hu, A. Akella, and C. J. Rossbach, "Towards a Machine Learning-Assisted Kernel with LAKE," in Proc. 28th ACM Int. Conf. Architectural Support for Programming Languages and Operating Systems, pp.846-861, 2023.
[2] H. Malallah, S. R. Zeebaree, R. R. Zebari, M. A. Sadeeq, Z. S. Ageed, I. M. Ibrahim, H. M. Yasin, and K. J. Merceedi, "A comprehensive study of kernel (issues and concepts) in different operating systems," Asian Journal of Research in Computer Science, Vol.8, No.3, pp.16-31, 2021.
[3] S. Krishnapriya and Y. Karuna, "A survey of deep learning for MRI brain tumor segmentation methods: Trends, challenges, and future directions," Health and Technology, Vol.13, No.2, pp.181-201, 2023.
[4] H. Lee, S. Jung, and H. Jo, "STUN: reinforcement-learning-based optimization of kernel scheduler parameters for static workload performance," Applied Sciences, Vol.12, No.14, pp.7072, 2022.
[5] H. Martin, M. Acher, J. A. Pereira, L. Lesoil, J.-M. Jézéquel, and D. E. Khelladi, "Transfer learning across variants and versions: The case of linux kernel size," IEEE Trans. Software Eng., Vol.48, No.11, pp.4274-4290, 2021.
[6] A. Hayat, "A Load-Balanced Task Scheduler for Heterogeneous Systems based on Machine Learning," M.S. thesis, CAPITAL UNIVERSITY, 2021.
[7] D. Singh, V. Bhalla, and N. Garg, "Load balancing algorithms with the application of machine learning: a review," MR Int. J. Eng. Technol., Vol.10, No.1, 2023.
[8] T. A. Rahmani, F. Daham, G. Belalem, and S. A. Mahmoudi, "HBalancer: A machine learning based load balancer in real time CPU-GPU heterogeneous systems," in Proc. 2022 Int. Conf. Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), IEEE, pp.674-679, 2022.
[9] Y. Qiu, H. Liu, T. Anderson, Y. Lin, and A. Chen, "Toward reconfigurable kernel datapaths with learned optimizations," in Proc. Workshop on Hot Topics in Operating Systems, pp.175-182, 2021.
[10] R. Mosaner, D. Leopoldseder, W. Kisling, L. Stadler, and H. Mössenböck, "Machine-Learning-Based Self-Optimizing Compiler Heuristics," in Proc. 19th Int. Conf. Managed Programming Languages and Runtimes, pp.98-111, 2022.
[11] Y. Kojima, R. Kazama, H. Abe, and C. Lee, "RNN-based Congestion Control in the Linux Kernel," in Proc. 2024 Twelfth Int. Symp. Computing and Networking Workshops (CANDARW), IEEE, pp.130-136, 2024.
[12] H. Qiu, W. Mao, C. W. H. Franke, Z. T. Kalbarczyk, T. Basar, and R. K. Iyer, "On the promise and challenges of foundation models for learning-based cloud systems management," in Workshop on Machine Learning for Systems at NeurIPS, Dec. 2023.
[13] S. Bian, C. Li, Y. Fu, Y. Ren, T. Wu, G. P. Li, and B. Li, "Machine learning-based real-time monitoring system for smart connected worker to improve energy efficiency," J. Manuf. Syst., Vol.61, pp.66-76, 2021.
[14] I. U. Akgun, A. S. Aydin, A. Shaikh, L. Velikov, and E. Zadok, "A machine learning framework to improve storage system performance," in Proc. 13th ACM Workshop on Hot Topics in Storage and File Systems , pp.94-102, 2021.
[15] V. K. Rayi, S. P. Mishra, J. Naik, and P. K. Dash, "Adaptive VMD based optimized deep learning mixed kernel ELM autoencoder for single and multistep wind power forecasting," Energy, Vol.244, pp.122585, 2022.
[16] V. Shankar, M. M. Deshpande, N. Chaitra, and S. Aditi, "Automatic detection of acute lymphoblastic leukemia using image processing," in Proc. 2016 IEEE Int. Conf. Advances in Computer Applications (ICACA), Coimbatore, India, pp.186-189, 2016. doi: 10.1109/ICACA.2016.7887948
[17] B. Herzog, F. Hügel, S. Reif, T. Hönig, and W. Schröder-Preikschat, "Automated selection of energy-efficient operating system configurations," in Proc. 12th ACM Int. Conf. Future Energy Systems, pp.309-315, 2021.
[18] V. Shankar, "Edge AI: A Comprehensive Survey of Technologies, Applications, and Challenges," in Proc. 2024 1st Int. Conf. Advanced Computing and Emerging Technologies (ACET), Ghaziabad, India, pp.1-6, 2024. doi: 10.1109/ACET61898.2024.10730112.
[19] J. Chen, S. S. Banerjee, Z. T. Kalbarczyk, and R. K. Iyer, "Machine Learning for Load Balancing in the Linux Kernel," in Proc. 11th ACM SIGOPS Asia-Pacific Workshop on Systems (APSys `20), pp.67-74, 2020. doi: 10.1145/3409963.3410492.
[20] C. Wang and J. Mou, "Linux Kernel Autotuning," in Proc. Linux Plumbers Conf., 2023.
[21] H. Dong, J. Appavoo, and S. Arora, "Tuning Linux Kernel Policies for Energy Efficiency with Machine Learning," Red Hat Research, 2023.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
