Next-generation Operating Systems: AI-driven Autonomy, Quantum Integration, and Edge Computing

Year : 2025 | Volume : 12 | Issue : 01 | Page : 25-36
    By

    Ushaa Eswaran,

  1. Principal and Professor, Department of Electronics and Communication Engineering, Mahalakshmi Tech Campus, Anna university, Chennai, Tamil Nadu, India

Abstract

The rapid evolution of computing paradigms is driving the need for next-generation operating systems that seamlessly integrate artificial intelligence, quantum computing, and edge processing. Traditional operating systems, while efficient for classical computation, lack the necessary capabilities to handle real-time AI-driven decision-making, quantum processing, and decentralized edge networks. This study explores how future operating systems will incorporate AI-driven autonomy to enhance resource management, quantum integration to leverage superior computational power, and edge computing to ensure low-latency processing. Through a detailed analysis of emerging methodologies, experimental results, and real-world case studies, this study provides insights into the technical advancements, challenges, and ethical considerations of futuristic operating systems.

Keywords: Next-generation operating systems, artificial intelligence, quantum integration, edge computing, AI-driven autonomy, decentralized computing, quantum algorithms, autonomous resource management

[This article belongs to Journal of Operating Systems Development & Trends ]

How to cite this article:
Ushaa Eswaran. Next-generation Operating Systems: AI-driven Autonomy, Quantum Integration, and Edge Computing. Journal of Operating Systems Development & Trends. 2025; 12(01):25-36.
How to cite this URL:
Ushaa Eswaran. Next-generation Operating Systems: AI-driven Autonomy, Quantum Integration, and Edge Computing. Journal of Operating Systems Development & Trends. 2025; 12(01):25-36. Available from: https://journals.stmjournals.com/joosdt/article=2025/view=203769


References

  1. Acharya DB, Kuppan K, Divya B. Agentic AI: Autonomous Intelligence for Complex Goals–A Comprehensive Survey. IEEE Access. 2025 Jan 22; 13: 18912–18936.
  2. Coccia M, Roshani S, Mosleh M. Evolution of quantum computing: Theoretical and innovation management implications for emerging quantum industry. IEEE Trans Eng Manag. 2022 Jun 20; 71: 2270–80.
  3. Yang R, Yu FR, Si P, Yang Z, Zhang Y. Integrated blockchain and edge computing systems: A survey, some research issues and challenges. IEEE Commun Surv Tutor. 2019 Jan 23; 21(2): 1508–32.
  4. Eswaran U, Khang A. Augmented Reality (AR) and Virtual Reality (VR) Technologies in Surgical Operating Systems. In AI and IoT Technology and Applications for Smart Healthcare Systems. Auerbach Publications; Florida, USA. 2024 May 15; 113–129.
  5. Sheta SV. Developing efficient server monitoring systems using AI for real-time data processing. International Journal of Engineering and Technology Research (IJETR). 2023 Jan–Dec; 8(1): 26–37.
  6. Ahmadi A. Quantum Computing and Artificial Intelligence: The Synergy of Two Revolutionary Technologies. Asian Journal of Electrical Sciences (AJES). 2023 Nov 2; 12(2): 15–27.
  7. Khakifirooz M, Fathi M, Dolgui A. Theory of AI-driven scheduling (TAIS): a service-oriented scheduling framework by integrating theory of constraints and AI. Int J Prod Res. 2024 Nov 6; 1–35.
  8. Eswaran U, Eswaran V. Augmented reality and virtual reality technologies in surgical operating systems. i-Manager’s Journal on Augmented & Virtual Reality (JAVR). 2023 Jan 1; 1(1): 9–17.
  9. Premsankar G, Di Francesco M, Taleb T. Edge computing for the Internet of Things: A case study. IEEE Internet Things J. 2018 Feb 12; 5(2): 1275–84.
  10. Angel NA, Ravindran D, Vincent PD, Srinivasan K, Hu YC. Recent advances in evolving computing paradigms: Cloud, edge, and fog technologies. Sensors. 2021 Dec 28; 22(1): 196.
  11. Eswaran U, Khang A. Artificial intelligence (ai)-aided computer vision (cv) in healthcare system. In Computer Vision and AI-Integrated IoT Technologies in the Medical Ecosystem. CRC Press; Florida, United States. 2024 Mar 29; 125–137.
  12. Bathla G, Bhadane K, Singh RK, Kumar R, Aluvalu R, Krishnamurthi R, Kumar A, Thakur RN, Basheer S. Autonomous vehicles and intelligent automation: Applications, challenges, and opportunities. Mob Inf Syst. 2022; 2022(1): 7632892.
  13. Sharma M, Tomar A, Hazra A. Edge computing for industry 5.0: Fundamental, applications and research challenges. IEEE Internet Things J. 2024 Mar 7; 11(11): 19070–19093.
  14. Alzu’Bi A, Alomar AA, Alkhaza’Leh S, Abuarqoub A, Hammoudeh M. A review of privacy and security of edge computing in smart healthcare systems: issues, challenges, and research directions. Tsinghua Sci Technol. 2024 Feb 9; 29(4): 1152–80.
  15. Mishra AK, Ravinder Reddy R, Tyagi AK, Arowolo MO. Artificial intelligence-enabled edge computing: Necessity of next generation future computing system. In IoT Edge Intelligence. Cham: Springer Nature Switzerland; 2024 Jun 4; 67–109.
Regular Issue Subscription Review Article
Volume 12
Issue 01
Received 27/02/2025
Accepted 28/02/2025
Published 18/03/2025
Publication Time 19 Days
Total Visits: 87


My IP

PlumX Metrics