Machine Learning Revolutionizing Server Management and Performance

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2025 | Volume : 16 | Issue : 02 | Page : –
    By

    Kazi Kutubuddin Sayyad Liyakat,

  1. Professor and Head, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India

Abstract

The modern data center is a complex and dynamic environment, grappling with ever-increasing workloads, stringent performance demands, and the constant pressure for cost optimization. As such, applying machine learning (ML) directly to the server infrastructure offers a powerful avenue for achieving advanced automation, resource optimization, and proactive problem resolution. This article explores the transformative potential of integrating machine learning into server systems, leveraging insights gleaned from the abstract and conclusion of a broader study on the subject. The application of machine learning to server systems is rapidly evolving, moving beyond simple monitoring and analysis to sophisticated predictive and optimization strategies. By analyzing vast streams of server telemetry data, containing CPU consumption, memory ingesting, network traffics, and disk I/O, machine learning algorithms can recognize patterns and irregularities which will be impossible for human operators to identify in real-time. This capability enables a range of applications, including dynamic resource allocation, predictive maintenance, and automated security threat detection. The promise lies in creating a more responsive, efficient, and resilient server infrastructure capable of adapting to fluctuating demands and unforeseen challenges. By embracing the transformative prospective of ML, organizations can unlock new era of efficiency and intelligence at the very core of their IT infrastructure. The future of data centers is undoubtedly intertwined with the evolution of intelligent servers powered by machine learning.

Keywords: Machine learning, servers, management, intelligent system, cloud computing

[This article belongs to Journal of Computer Technology & Applications ]

How to cite this article:
Kazi Kutubuddin Sayyad Liyakat. Machine Learning Revolutionizing Server Management and Performance. Journal of Computer Technology & Applications. 2025; 16(02):-.
How to cite this URL:
Kazi Kutubuddin Sayyad Liyakat. Machine Learning Revolutionizing Server Management and Performance. Journal of Computer Technology & Applications. 2025; 16(02):-. Available from: https://journals.stmjournals.com/jocta/article=2025/view=208028



References

  1. Khadake S, Kawade S, Moholkar S, Pawar M. A review of 6G technologies and its advantages over 5G technology. InTechno-Societal 2016, International Conference on Advanced Technologies for Societal Applications 2022 Dec 9 (pp. 1043-1051). Cham: Springer International Publishing.
  2. Patil VJ, Khadake SB, Tamboli DA, Mallad HM, Takpere SM, Sawant VA. Review of AI in power electronics and drive systems. In2024 3rd International conference on Power Electronics and IoT Applications in Renewable Energy and its Control (PARC) 2024 Feb 23 (pp. 94-99). IEEE.
  3. Dudgikar AB, Ingalgi AA, Jamadar AG, Swami OR, Khadake SB, Moholkar SV. Intelligent battery swapping system for electric vehicles with charging stations locator on IoT and cloud platform. International Journal of Advanced Research in Science, Communication and Technology. 2023 Jan;3(1):204-8.
  4. Javagar P, Surendar V, Jayakumar K, Riyas KA, Dhanush K. Solar charging power station for electric vehicle. In2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) 2022 Apr 7 (pp. 824-827). IEEE.
  5. Zohuri B, Mossavar-Rahmani F. The symbiotic relationship unraveling the interplay between technology and artificial intelligence (an intelligent dynamic relationship). Journal of Energy and Power Engineering. 2023 Apr;17(2).
  6. Zhong F, Wang J, Cheng X. Application of Intelligent Traffic Control Based on PLC. InConference of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013) 2013 Mar (pp. 1044-1048). Atlantis Press.
  7. Magar SS, Sugandhi AS, Pawar SH, Khadake SB, Mallad HM. Harnessing Wind Vibration, a Novel Approach towards Electric Energy Generation-Review. IJARSCT. 2024 Oct;4(2):73-82.
  8. Abass N, Vaidya R, Satav R, Jeyavel J. Automatic sanitizer dispenser with temperature screening. International Journal of Advance Research, Ideas and Innovations in Technology. 2021;7(3):283- 5.
  9. Dhawale A, Kamble A, Ghule S, Gawande M. Solar Powered Indoor Air Purifier With Air Quality Monitor. In2023 9th International Conference on Electrical Energy Systems (ICEES) 2023 Mar 23 (pp. 206-209). IEEE.
  10. Shabnam S, Latha HN. Design and implementation of saliency detection model in h. 264 standard. International Journal of Science and Research (IJSR). 2014:2014-20.
  11. Muddamsetty SM, Sidibé D, Trémeau A, Mériaudeau F. Salient objects detection in dynamic scenes using color and texture features. Multimedia Tools and Applications. 2018 Mar;77:5461-74.
  12. Guleria M, Rathi A, Sajwan DS, Negi JS, Chauhan N. DTMF based irrigation water pump control system. Int. J. Smart Device Appl. 2017 Jan;5(1):19-24.
  13. Arulious JA, Earlina D, Harish D, Priya PS, Rexy AI, Mary JN. Design of solar powered electric vehicle. InJournal of Physics: Conference Series 2021 Nov 1 (Vol. 2070, No. 1, p. 012105). IOP Publishing.
  14. Kumar TP, Haidari M, Vimala R, Subramanya R. Low-cost Battery Management System For E- vehicles. In2022 International Conference on Edge Computing and Applications (ICECAA) 2022 Oct 13 (pp. 723-727). IEEE.
  15. Chounde A, Gopnarayan BB, Patil KB, Kamble SS. Human Health Care System: A New Approach towards Life. Grenze International Journal of Engineering & Technology (GIJET). 2024 Jun 15;10.
  16. Patil VJ, Mallad HM, Gopnarayan BB, Pati KB. Maximize Farming Productivity through Agriculture 4.0 based Intelligence, with use of Agri Tech Sense Advanced Crop Monitoring System. Grenze International Journal of Engineering & Technology (GIJET). 2024 Jun 15;10.
  17. Atabani AE. Biodiesel: a promising alternative energy resource. Alternative Fuels Research Progress (93 pp). International Energy and Environmental Foundation Publishers. 2013.
  18. Veena C, Sridevi M, Liyakat KK, Saha B, Reddy SR, Shirisha N. HEECCNB: An efficient IoT- cloud architecture for secure patient data transmission and accurate disease prediction in healthcare systems. In2023 Seventh International Conference on Image Information Processing (ICIIP) 2023 Nov 22 (pp. 407-410). IEEE.
  19. Kazi KS. Computer-aided diagnosis in ophthalmology: A technical review of deep learning applications. Transformative Approaches to Patient Literacy and Healthcare Innovation. 2024:112- 35.
  20. Kazi KS. AI-Driven-IoT (AIIoT)-Based Decision Making in Kidney Diseases Patient Healthcare Monitoring: KSK Approach for Kidney Monitoring. InAI-Driven Innovation in Healthcare Data Analytics 2025 (pp. 277-306). IGI Global Scientific Publishing.
  21. Pradeepa M, Jamberi K, Sajith S, Bai MR, Prakash A. Student health detection using a machine learning approach and IoT. In2022 IEEE 2nd Mysore sub section International Conference (MysuruCon) 2022 Oct 16 (pp. 1-5). IEEE.
  22. Aldahiri A, Alrashed B, Hussain W. Trends in using IoT with machine learning in health prediction system. Forecasting. 2021 Mar 7;3(1):181-206.
  23. Mulani AO, Sardey MP, Kinage K, Salunkhe SS, Fegade T, Fegade PG. ML-powered Internet of Medical Things (MLIOMT) structure for heart disease prediction. Journal of Pharmacology and Pharmacotherapeutics. 2025 Mar;16(1):38-45.
  24. Payalan YF, Guvensan MA. Towards next-generation vehicles featuring the vehicle intelligence. IEEE Transactions on Intelligent Transportation Systems. 2019 Jun 11;21(1):30-47.
  25. Patel PP, Pavesha CL, Sabat SS, More SS. Deep learning based driver drowsiness detection. In2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC) 2022 May 9 (pp. 380-386). IEEE.
  26. Yahaya L, Oye ND, Garba EJ. A comprehensive review on heart disease prediction using data mining and machine learning techniques. American Journal of Artificial Intelligence. 2020 Apr;4(1):20-9.
  27. Kutubuddin KA, Nerkar PR, Sultanabanu KA. IoT-based skin health monitoring system. International Journal of Biology, Pharmacy and Allied Sciences. 2024;13(11):5937-50.
  28. Sreelakshmy R, Sruthy R, Rajeshwari R, Thyla B. Patient health monitoring system using smart IoT devices for medical emergency services. In2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) 2022 Jul 15 (pp. 1- 10). IEEE.
  29. Malathi S, Razool Begum S. Architecture for Reliable Cyber Attack Detection in IOT Networks to Increase the Trust Worthiness Between Nodes. International Journal of Modeling, Simulation, and Scientific Computing. 2024 Aug 22.
Regular Issue Subscription Review Article
Volume 16
Issue 02
Received 15/02/2025
Accepted 19/03/2025
Published 15/04/2025
Publication Time 59 Days
Total Visits: 30


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