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.
- [1] . 2020. A survey on beyond 5G network with the advent of 6G: Architecture and emerging technologies. IEEE Access 9 (2020), 67512–67547.Google Scholar
Cross Ref
- [2] . 2020. Expansive networks: Exploiting spectrum sharing for capacity boost and 6G vision. J. Commun. Netw. 22, 6 (2020), 444–454.Google Scholar
Cross Ref
- [3] . 2020. 6G technology based advanced virtual multi-purpose embedding algorithm to solve far-reaching network effects. Comput. Commun. 160 (2020), 749–758.Google Scholar
Cross Ref
- [4] . 2020. A taxonomy of AI techniques for 6G communication networks. Comput. Commun. 161 (2020), 279–303.Google Scholar
Cross Ref
- [5] . 2021. Design and feasibility verification of 6G wireless communication systems with state of the art technologies. Int. J. Wireless Inf. Networks, 1–25.Google Scholar
- [6] . 2021. Application of cybertwin for offloading in mobile multiaccess edge computing for 6G networks. IEEE IoT J. 8, 22 (2021), 16231–16242.Google Scholar
- [7] . 2021. Joint virtual network topology design and embedding for cybertwin-enabled 6G core networks. IEEE IoT J. 8, 22 (2021), 16313–16325.Google Scholar
- [8] . 2020. Blockchain-enabled resource management and sharing for 6G communications. Digit. Commun. Netw. 6, 3 (2020), 261–269.Google Scholar
Cross Ref
- [9] . 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 Scholar
- [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 Scholar
- [11] . 2021. Decentralized federated learning for extended sensing in 6G connected vehicles. Vehic. Commun. (2021), 100396.Google Scholar
- [12] . 2021. URLLC resource slicing and scheduling for trustworthy 6G vehicular services: A federated reinforcement learning approach. Phys. Commun. 49 (2021), 101470.Google Scholar
Digital Library
- [13] . 2021. 6G-LEGO: A framework for 6G network slices. J. Commun. Netw. (2021)Google Scholar
Cross Ref
- [14] . 2021. Transition technologies towards 6G networks. EURASIP J. Wireless Commun. Network. 1 (2021), 1–22.Google Scholar
- [15] . 2021. Multi-agent reinforcement learning-based resource management for end-to-end network slicing. IEEE Access 9 (2021), 56178–56190.Google Scholar
Cross Ref
- [16] . 2019. A multi-clustering approach to scale distributed tenant networks for mobile edge computing. IEEE J. Select. Areas Commun. 37, 3 (2019), 499–514.Google Scholar
Cross Ref
- [17] . 2021. Adaptive network slicing in multi-tenant 5G IoT networks. IEEE Access 9 (2021), 14048–14069.Google Scholar
Cross Ref
- [18] . 2020. An intelligent self-sustained RAN slicing framework for diverse service provisioning in 5G-beyond and 6G networks. Intell. Converg. Netw. 1, 3 (2020), 281–294.Google Scholar
Cross Ref
- [19] . 2021. Intelligent radio access network slicing for service provisioning in 6G: A hierarchical deep reinforcement learning approach. IEEE Trans. Commun. (2021).Google Scholar
Cross Ref
- [20] . 2021. Resource-Ability Assisted Service Function Chain Embedding and Scheduling for 6G Networks With Virtualization. IEEE Trans. Vehic. Technol. 70, 4 (2021), 3846–3859.Google Scholar
Cross Ref
- [21] . 2021. A genetic algorithm approach for service function chain placement in 5G and beyond, virtualized edge networks. Comput. Netw. 195 (2021), 108157.Google Scholar
Cross Ref
- [22] . 2021. Energy and performance-efficient task scheduling in heterogeneous virtualized cloud computing. Sust. Comput.: Inf. Syst. 30 (2021), 100517.Google Scholar
Cross Ref
- [23] . 2019. Latency-aware virtualized network function provisioning for distributed edge clouds. J. Syst. Softw. 152 (2019), 24–31.Google Scholar
Digital Library
- [24] . 2020. Efficient algorithms to minimize the end-to-end latency of edge network function virtualization. J. Amb. Intell. Human. Comput. (2020), 1–12.Google Scholar
- [25] . 2020. LW-CoEdge: A lightweight virtualization model and collaboration process for edge computing. World Wide Web 23, 2 (2020), 1127–1175.Google Scholar
Cross Ref
- [26] . 2021. A network operator-biased approach for multi-service network function placement in a 5G network slicing architecture. Comput. Netw. 201 (2021), 108598.Google Scholar
Digital Library
- [27] . 2021. The 5G network slicing using SDN based technology for managing network traffic. Proc. Comput. Sci. 194 (2021), 114–121.Google Scholar
Digital Library
- [28] . 2020. Real-time maintenance of latency-sensitive 5G services through network slicing. Photon. Netw. Commun. 40, 3 (2020), 221–232.Google Scholar
Digital Library
- [29] . 2020. Dynamic network slicing and resource allocation in mobile edge computing systems. IEEE Trans. Vehic. Technol. 69, 7 (2020), 7863–7878.Google Scholar
Cross Ref
- [30] https://www.kaggle.com/datasets/anuragthantharate/deepslice.Google Scholar
- [31] . 2022. Densely enhanced semantic network for conversation system in social Media. ACM Trans. Multimedia Comput. Commun. Appl. 18, 4 (2022), 1–24.Google Scholar
Digital Library
- [32] . 2022. Multi-granularity brushstrokes network for universal style transfer. ACM Trans. Multimedia Comput. Commun. Appl. 18, 4 (2022), 1–17.Google Scholar
Digital Library
- [33] . 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), 1–22.Google Scholar
Digital Library
- [34] . 2022. A novel multi-sample generation method for adversarial attacks. ACM Trans. Multimedia Comput. Commun. Appl. 18, 4 (2022), 1–21.Google Scholar
Digital Library
- [35] . 2022. Shuffle-invariant network for action recognition in videos. ACM Trans. Multimedia Comput. Commun. Appl. 18, 3 (2022), 1–18.Google Scholar
Digital Library
Index Terms
A Normalized Slicing-assigned Virtualization Method for 6G-based Wireless Communication Systems
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