Exploring the Future of Operating Systems: Architectural Innovations and Kernel Development Trends

Year : 2024 | Volume : 11 | Issue : 03 | Page : 38 47
    By

    Ashish Singh,

  • Preetam Soni,

  • Kapil Vijay Javalgekar,

  • Aniket Anand,

  • Mritunjay Kr. Ranjan,

  • Shilpi Saxena,

  1. Student, School of Computer Sciences and Engineering, Sandip University, Nashik, Maharashtra, India
  2. Chief Technical Officer, Anishk Sustainable Development Foundation, Korba, Chhattisgarh, India
  3. Assistant Professor, School of Computer Sciences and Engineering, Sandip University, Nashik, Maharashtra, India
  4. Scholar, Department of Computer Science and Information Technology, Magadh University, Bodh Gaya, Bihar, India
  5. Assistant Professor, School of Computer Sciences and Engineering, Sandip University, Nashik, Maharashtra, India
  6. Assistant Professor, Department of Computer Application and IT, Lords University, Alwar, Rajasthan, India

Abstract

Modern applications and the rapid evolution of hardware technologies are challenging operating system (OS) design. This paper speculates the future of OS based on revolutionary architecture advancements and emerging possibilities in kernel construction. The growth of multi-core processors, spread-bound processing, and edge architectures have challenged traditional OS paradigms. The paper provides an analysis of the progress in microkernel and monolithic kernel structures, discussing the bandwidth capacity as well as security effectiveness. Also, it analyses the effect these changes had on operating system architecture: virtualization containerization real-time processing. It also discusses the possible future directions of increased system efficiency and flexibility, by incorporating AI-driven optimization and autonomous resource management within OS kernels. Additionally, the paper discusses how quantum computing and non-volatile memory technologies will determine future OS designs. As it is a measure of these advancements, the research sheds light on how upcoming operating systems can fulfill exceptional technology needs to accommodate greater resource efficiency and agility for enhanced user experience. In addition, the paper also checks for the implications of AI in OS design. AI provides more robust frameworks to facilitate communication between AI algorithms and hardware components.

Keywords: Operating systems, kernel development, microkernel, virtualization, quantum computing, AI optimization

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

How to cite this article:
Ashish Singh, Preetam Soni, Kapil Vijay Javalgekar, Aniket Anand, Mritunjay Kr. Ranjan, Shilpi Saxena. Exploring the Future of Operating Systems: Architectural Innovations and Kernel Development Trends. Journal of Operating Systems Development & Trends. 2024; 11(03):38-47.
How to cite this URL:
Ashish Singh, Preetam Soni, Kapil Vijay Javalgekar, Aniket Anand, Mritunjay Kr. Ranjan, Shilpi Saxena. Exploring the Future of Operating Systems: Architectural Innovations and Kernel Development Trends. Journal of Operating Systems Development & Trends. 2024; 11(03):38-47. Available from: https://journals.stmjournals.com/joosdt/article=2024/view=180712


References

  1. Milojicic D. Operating Systems—Now and in the Future. IEEE Concurrency. 1999;7:12–21. DOI: 10.1109/MCC.1999.749132.
  2. Chen A. A review of emerging non-volatile memory (NVM) technologies and applications. Solid-State Electronics. 2016;125:25–38. DOI: 10.1016/j.sse.2016.07.006.
  3. Koponen T, Shenker S, Balakrishnan H, Feamster N, Ganichev I, Ghodsi A, et al. Architecting for innovation. ACM Sigcomm Computer Communication Review. 2011;41:24–36. DOI: 10.1145/2002250.2002256.
  4. Zhang Y, Zhao X, Yin J, Zhang L, Chen Z. Operating System and Artificial Intelligence: A Systematic Review. [Preprint]. ArXiv:2407.14567. 2024 Jul 19. DOI: 10.48550/arXiv.2407.14567.
  5. Baumann A, Barham P, Dagand PE, Harris T, Isaacs R, Peter S, Roscoe T, Schüpbach A, Singhania A. The multikernel: a new OS architecture for scalable multicore systems. In: Proceedings of the ACM SIGOPS 22nd Symposium on Operating Systems Principles; 2009; Big Sky, Montana, USA. New York, NY: Association for Computing Machinery; 2009. p. 29–44. DOI: 10.1145/1629575.1629579.
  6. Wang SP. Computer Architecture and Organization: Fundamentals and Architecture Security. Singapore: Springer Singapore; 2021. DOI: 10.1007/978-981-16-5662-0.
  7. Xiao J, Huang H, Wang H. Kernel data attack is a realistic security threat. In: Thuraisingham B, Wang X, Yegneswaran V, editors. Security and Privacy in Communication Networks. 11th International Conference, SecureComm 2015, Dallas, TX, USA, October 26–29, 2015, Revised Selected Papers. Cham: Springer; 2016. p. 135–54. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering; vol 186). DOI: 10.1007/978-3-319-28865-9_8.
  8. Rasheed S, Mazhar S, Naqvi MR. Highlighting demanding aspects of operating systems for improved efficiency. 2021 International Conference on Data Analytics for Business and Industry (ICDABI), Sakheer, Bahrain. 2021. pp. 599–603. DOI: 10.1109/ICDABI53623.2021.9655871.
  9. Gordon N, Pedretti K, Lange JR. Porting the Kitten Lightweight Kernel Operating System to RISC-V. 2022 IEEE/ACM International Workshop on Runtime and Operating Systems for Supercomputers (ROSS), Dallas, TX, USA. 2022. pp. 1–7. DOI: 10.1109/ROSS56639.2022.00008.
  10. Sharma R, Sandhu J, Bharti V. Exploring feature-based image classification for human identification in multimodal biometric system. 2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India. 2024. pp. 1–6. DOI: 10.1109/ICRITO61523.2024.10522307.
  11. Wei J, Yang W, Ye P, Li W, Liao Y, Ding K, et al. Influence of capacity configuration on stability of hydropower-photovoltaic hybrid energy systems. 2023 IEEE 7th Conference on Energy Internet and Energy System Integration (EI2), Hangzhou, China. 2023. pp. 1000–5. DOI: 10.1109/EI259745.2023.10512580.
  12. Shi E, Zhang J, Du H, Ai B, Yuen C, Niyato D, et al. RIS-aided cell-free massive MIMO systems for 6G: Fundamentals, system design, and applications. Proceedings of the IEEE. 2024;112:331–64. DOI: 10.1109/JPROC.2024.3404491.
  13. Mukherjee SS, Weaver C, Emer J, Reinhardt SK, Austin T. A systematic methodology to compute the architectural vulnerability factors for a high-performance microprocessor. Proceedings. 36th Annual IEEE/ACM International Symposium on Microarchitecture, 2003. MICRO-36., San Diego, CA, USA. 2003. pp. 29–40. DOI: 10.1109/MICRO.2003.1253181.
  14. Razouk RR, Stewart T, Wilson M. Measuring operating system performance on modern micro-processors. In: Proceedings of the 1986 ACM SIGMETRICS Joint International Conference on Computer Performance Modelling, Measurement and Evaluation. New York, NY: Association for Computing Machinery; 1986. p. 193–202. DOI: 10.1145/317499.317552.
  15. Topcuoglu H, Hariri S, Wu MY. Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans Parallel Distrib Syst. 2002;13:260–74. DOI: 10.1109/71.993206
  16. Sattar AM, Soni P, Ranjan MK, Kumar A, Sahu C, Saxena S, et al. Accelerating cross-platform development with Flutter framework. J Open Source Dev. 2023;10:1–11. DOI: 10.37591/joosd.v10i2.580.
  17. Ahmad N, Javaid N, Mehmood M, Hayat M, Ullah A, Khan HA. Fog-cloud based platform for utilization of resources using load balancing technique. In: Barolli L, Kryvinska N, Enokido T, Takizawa M, editors. Advances in Network-Based Information Systems. NBiS 2018. Lecture Notes on Data Engineering and Communications Technologies, vol 22. Cham: Springer; 2019. pp. 554–67. DOI: 10.1007/978-3-319-98530-5_48.
  18. Li D, Zhang Z, Liao W, Xu Z. KLRA: A Kernel Level Resource Auditing Tool for IoT Operating System Security. 2018 IEEE/ACM Symposium on Edge Computing (SEC), Seattle, WA, USA, 2018. pp. 427–32. doi: 10.1109/SEC.2018.00058.
  19. Shropshire J. Analysis of monolithic and microkernel architectures: Towards secure hypervisor design. 2014 47th Hawaii International Conference on System Sciences, Waikoloa, HI, USA. 2014. pp. 5008–17. DOI: 10.1109/HICSS.2014.615.
  20. Lu S, Lin Z, Zhang M. Kernel vulnerability analysis: A survey. 2019 IEEE Fourth International Conference on Data Science in Cyberspace (DSC), Hangzhou, China. 2019. pp. 549–54. DOI: 10.1109/DSC.2019.00089.
  21. Montecchi L, Nostro N, Ceccarelli A, Vella G, Caruso A, Bondavalli A. Model-based evaluation of scalability and security tradeoffs: A case study on a multi-service platform. Electron Notes Theor Comput Sci. 2015;310:113–33. DOI: 10.1016/j.entcs.2014.12.015.
  22. Gerofi B, Ishikawa Y, Riesen R, Wisniewski RW, Park Y, Rosenburg B. A multi-kernel survey for high-performance computing. In: Proceedings of the 6th International Workshop on Runtime and Operating Systems for Supercomputers. New York, NY: Association for Computing Machinery; 2016. p. 1–8. DOI: 10.1145/2931088.2931092.
  23. Novković B, Golub M. Improving monolithic kernel security and robustness through intra-kernel sandboxing. Computers and Security. 2023;127:103104. DOI: 10.1016/j.cose.2023.103104.
  24. Pendleton M, Garcia-Lebron R, Cho JH, Xu S. A survey on systems security metrics. ACM Computing Surveys. 2017;49:1–35. DOI: 10.1145/3005714.
  25. Jimenez M, Papadakis M, Le Traon YL. Vulnerability prediction models: A case study on the Linux kernel. 2016 IEEE 16th International Working Conference on Source Code Analysis and Manipulation (SCAM), Raleigh, NC, USA, 2016. pp. 1–10. DOI: 10.1109/SCAM.2016.15.
Regular Issue Subscription Review Article
Volume 11
Issue 03
Received 22/10/2024
Accepted 23/10/2024
Published 04/11/2024
532


My IP

PlumX Metrics