skip to main content
research-article

A Normalized Slicing-assigned Virtualization Method for 6G-based Wireless Communication Systems

Authors Info & Claims
Published:01 November 2022Publication History
Skip Abstract Section

Abstract

The next generation of wireless communication systems will rely on advantageous sixth-generation wireless network (6G) features and sophisticated edge Internet-of-Things technology to provide continuous service delegation and resource allocation. Network slicing and virtualization are common in these scenarios to meet user demands and application services. This article introduces a Normalized Slicing-assigned Virtualization Method for satisfying the 6G features in future generation systems. The proposed method relies on available resource roots and time intervals for replications. Based on the availability and Accessibility, the resource virtualization and network slicing processes are forwarded. The proposed method exploits federated learning for determining availability and accessibility models in detecting slicing, virtualization, or both the requirements throughout the resource sharing process. This improves the resource sharing rate, with less latency and high processing despite the user and application demands. The learning models are trained to balance replication and network slicing for confining complexity across different resources. The proposed method's performance is validated using the above metrics for varying users and intervals.

REFERENCES

  1. [1] Dogra A., Jha R. K., and Jain S.. 2020. A survey on beyond 5G network with the advent of 6G: Architecture and emerging technologies. IEEE Access 9 (2020), 6751267547.Google ScholarGoogle ScholarCross RefCross Ref
  2. [2] Gür G.. 2020. Expansive networks: Exploiting spectrum sharing for capacity boost and 6G vision. J. Commun. Netw. 22, 6 (2020), 444454.Google ScholarGoogle ScholarCross RefCross Ref
  3. [3] Saad A., Al-Ma'aitah M., and Alwadain A.. 2020. 6G technology based advanced virtual multi-purpose embedding algorithm to solve far-reaching network effects. Comput. Commun. 160 (2020), 749758.Google ScholarGoogle ScholarCross RefCross Ref
  4. [4] Sheth K., Patel K., Shah H., Tanwar S., Gupta R., and Kumar N.. 2020. A taxonomy of AI techniques for 6G communication networks. Comput. Commun. 161 (2020), 279303.Google ScholarGoogle ScholarCross RefCross Ref
  5. [5] Dilli R.. 2021. Design and feasibility verification of 6G wireless communication systems with state of the art technologies. Int. J. Wireless Inf. Networks, 125.Google ScholarGoogle Scholar
  6. [6] Rodrigues T. K., Liu J., and Kato N.. 2021. Application of cybertwin for offloading in mobile multiaccess edge computing for 6G networks. IEEE IoT J. 8, 22 (2021), 1623116242.Google ScholarGoogle Scholar
  7. [7] Li J., Shi W., Ye Q., Zhang S., Zhuang W., and Shen X.. 2021. Joint virtual network topology design and embedding for cybertwin-enabled 6G core networks. IEEE IoT J. 8, 22 (2021), 1631316325.Google ScholarGoogle Scholar
  8. [8] Xu H., Klaine P. V., Onireti O., Cao B., Imran M., and Zhang L.. 2020. Blockchain-enabled resource management and sharing for 6G communications. Digit. Commun. Netw. 6, 3 (2020), 261269.Google ScholarGoogle ScholarCross RefCross Ref
  9. [9] Liu C., Feng W., Tao X., and Ge N.. 2021. MEC-empowered non-terrestrial network for 6G wide-area time-sensitive internet of things. arXiv:2103.11907. Retrieved from https://arxiv.org/abs/2103.11907.Google ScholarGoogle Scholar
  10. [10] L. Jiang, X. Chang, J. Mišić, V. B. Mišić, and J. Bai. 2022. Understanding MEC empowered vehicle task offloading performance in 6G networks. Peer-to-Peer Networking and Applications 15, 2 (2022), 1090--1104.Google ScholarGoogle Scholar
  11. [11] Barbieri L., Savazzi S., Brambilla M., and Nicoli M.. 2021. Decentralized federated learning for extended sensing in 6G connected vehicles. Vehic. Commun. (2021), 100396.Google ScholarGoogle Scholar
  12. [12] Hao M., Ye D., Wang S., Tan B., and Yu R.. 2021. URLLC resource slicing and scheduling for trustworthy 6G vehicular services: A federated reinforcement learning approach. Phys. Commun. 49 (2021), 101470.Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. [13] Kukliński S., Tomaszewski L., Kolakowski R., and Chemouil P.. 2021. 6G-LEGO: A framework for 6G network slices. J. Commun. Netw. (2021)Google ScholarGoogle ScholarCross RefCross Ref
  14. [14] Raddo T. R., Rommel S., Cimoli B., Vagionas C., Perez-Galacho D., Pikasis E., and Monroy I. T.. 2021. Transition technologies towards 6G networks. EURASIP J. Wireless Commun. Network. 1 (2021), 122.Google ScholarGoogle Scholar
  15. [15] Kim Y. and Lim H.. 2021. Multi-agent reinforcement learning-based resource management for end-to-end network slicing. IEEE Access 9 (2021), 5617856190.Google ScholarGoogle ScholarCross RefCross Ref
  16. [16] Bruschi R., Davoli F., Lago P., and Pajo J. F.. 2019. A multi-clustering approach to scale distributed tenant networks for mobile edge computing. IEEE J. Select. Areas Commun. 37, 3 (2019), 499514.Google ScholarGoogle ScholarCross RefCross Ref
  17. [17] Escolar A. M., Alcaraz-Calero J. M., Salva-Garcia P., Bernabe J. B., and Wang Q.. 2021. Adaptive network slicing in multi-tenant 5G IoT networks. IEEE Access 9 (2021), 1404814069.Google ScholarGoogle ScholarCross RefCross Ref
  18. [18] Mei J., Wang X., and Zheng K.. 2020. An intelligent self-sustained RAN slicing framework for diverse service provisioning in 5G-beyond and 6G networks. Intell. Converg. Netw. 1, 3 (2020), 281294.Google ScholarGoogle ScholarCross RefCross Ref
  19. [19] Mei J., Wang X., Zheng K., Boudreau G., Sediq A. B., and Abou-zeid H.. 2021. Intelligent radio access network slicing for service provisioning in 6G: A hierarchical deep reinforcement learning approach. IEEE Trans. Commun. (2021).Google ScholarGoogle ScholarCross RefCross Ref
  20. [20] Cao H., Du J., Zhao H., Luo D. X., Kumar N., Yang L., and Yu F. R.. 2021. Resource-Ability Assisted Service Function Chain Embedding and Scheduling for 6G Networks With Virtualization. IEEE Trans. Vehic. Technol. 70, 4 (2021), 38463859.Google ScholarGoogle ScholarCross RefCross Ref
  21. [21] Magoula L., Barmpounakis S., Stavrakakis I., and Alonistioti N.. 2021. A genetic algorithm approach for service function chain placement in 5G and beyond, virtualized edge networks. Comput. Netw. 195 (2021), 108157.Google ScholarGoogle ScholarCross RefCross Ref
  22. [22] Hussain M., Wei L. F., Lakhan A., Wali S., Ali S., and Hussain A.. 2021. Energy and performance-efficient task scheduling in heterogeneous virtualized cloud computing. Sust. Comput.: Inf. Syst. 30 (2021), 100517.Google ScholarGoogle ScholarCross RefCross Ref
  23. [23] Son J. and Buyya R.. 2019. Latency-aware virtualized network function provisioning for distributed edge clouds. J. Syst. Softw. 152 (2019), 2431.Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. [24] Ghai K. S., Choudhury S., and Yassine A.. 2020. Efficient algorithms to minimize the end-to-end latency of edge network function virtualization. J. Amb. Intell. Human. Comput. (2020), 112.Google ScholarGoogle Scholar
  25. [25] Alves M. P., Delicato F. C., Santos I. L., and Pires P. F.. 2020. LW-CoEdge: A lightweight virtualization model and collaboration process for edge computing. World Wide Web 23, 2 (2020), 11271175.Google ScholarGoogle ScholarCross RefCross Ref
  26. [26] Shinde S. S., Marabissi D., and Tarchi D.. 2021. A network operator-biased approach for multi-service network function placement in a 5G network slicing architecture. Comput. Netw. 201 (2021), 108598.Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. [27] Alotaibi D., Thayananthan V., and Yazdani J.. 2021. The 5G network slicing using SDN based technology for managing network traffic. Proc. Comput. Sci. 194 (2021), 114121.Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. [28] Montero R., Agraz F., Pagès A., and Spadaro S.. 2020. Real-time maintenance of latency-sensitive 5G services through network slicing. Photon. Netw. Commun. 40, 3 (2020), 221232.Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. [29] Feng J., Pei Q., Yu F. R., Chu X., Du J., and Zhu L.. 2020. Dynamic network slicing and resource allocation in mobile edge computing systems. IEEE Trans. Vehic. Technol. 69, 7 (2020), 78637878.Google ScholarGoogle ScholarCross RefCross Ref
  30. [30] https://www.kaggle.com/datasets/anuragthantharate/deepslice.Google ScholarGoogle Scholar
  31. [31] Li Y., Wang Z., and Yu J.. 2022. Densely enhanced semantic network for conversation system in social Media. ACM Trans. Multimedia Comput. Commun. Appl. 18, 4 (2022), 124.Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. [32] Wang Q., Li S., Zhang X., and Feng G.. 2022. Multi-granularity brushstrokes network for universal style transfer. ACM Trans. Multimedia Comput. Commun. Appl. 18, 4 (2022), 117.Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. [33] Wu J., Jiang J., Qi M., Chen C., and Zhang J.. 2022. An End-to-end heterogeneous restraint network for RGB-D Cross-modal person Re-identification. ACM Trans. Multimedia Comput. Commun. Appl. 18, 4 (2022), 122.Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. [34] Duan M., Li K., Deng J., Xiao B., and Tian Q.. 2022. A novel multi-sample generation method for adversarial attacks. ACM Trans. Multimedia Comput. Commun. Appl. 18, 4 (2022), 121.Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. [35] Shi Q., Zhang H. B., Li Z., Du J. X., Lei Q., and Liu J. H.. 2022. Shuffle-invariant network for action recognition in videos. ACM Trans. Multimedia Comput. Commun. Appl. 18, 3 (2022), 118.Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. A Normalized Slicing-assigned Virtualization Method for 6G-based Wireless Communication Systems

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in

        Full Access

        • Published in

          cover image ACM Transactions on Multimedia Computing, Communications, and Applications
          ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 18, Issue 3s
          October 2022
          381 pages
          ISSN:1551-6857
          EISSN:1551-6865
          DOI:10.1145/3567476
          • Editor:
          • Abdulmotaleb El Saddik
          Issue’s Table of Contents

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 1 November 2022
          • Online AM: 18 July 2022
          • Accepted: 23 June 2022
          • Revised: 24 May 2022
          • Received: 27 February 2022
          Published in tomm Volume 18, Issue 3s

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article
          • Refereed
        • Article Metrics

          • Downloads (Last 12 months)236
          • Downloads (Last 6 weeks)11

          Other Metrics

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        Full Text

        View this article in Full Text.

        View Full Text

        HTML Format

        View this article in HTML Format .

        View HTML Format
        About Cookies On This Site

        We use cookies to ensure that we give you the best experience on our website.

        Learn more

        Got it!