Integrating Auction-Based Mechanisms For Enhanced Resource Management In 5G Slicing
DOI:
https://doi.org/10.29284/rap78m64Keywords:
Network Slicing, Fifth Generation Cellular Networks, Auction-Based Decision.Abstract
Fifth-generation computer networks (5G) are designed to accommodate a diverse range of heterogeneous and often incompatible services. Unlike traditional networks, the "one-size-fits-all" approach is not applicable to 5G. To enable a seamless transition to this new generation, several theories have been explored, with network slicing being among the most critical. Network slicing involves creating and managing multiple logical networks, which are then mapped to a physical infrastructure. Each slice is characterized by a unique set of network functions and resources, allowing specific services to be assigned to individual slices. This approach ensures that the network functions and resources of each slice are tailored to meet the specific requirements of the assigned service. Network slicing has attracted significant research interest, but this study focuses primarily on resource management within network slices. Resource management is broadly categorized into two key areas: "slice admission control" and "cross-slice resource allocation." From a decision-making perspective, these areas employ two main approaches: policy-based decisions and auction-based decisions. This research combines both slice admission control and cross-slice resource allocation using an auction-based decision-making framework, which, to the best of our knowledge, is a novel approach. While slice admission control and cross-slice resource allocation may appear distinct, they are closely interconnected, making it essential to study and combine them together with an appropriate technique. This study combines the mentioned methods and examines the impact of dynamic cross-slice resource allocation on slice admission control. Through simulations and analysis, the research demonstrates how these mechanisms collectively enhance resource management in fifth-generation computer networks.
References
1. Osseiran++, Afif, Jose F. Monserrat, and Patrick Marsch, eds. 5G mobile and wireless communications technology. Cambridge University Press, 2016.
2. Ict, M. "317669-METIS/D1. Scenarios requirements and KPIs for 5G mobile and wireless system." (2013).
3. International Telecommunications Union Radio (ITU-R), “Requirements related to technical performance for IMT-Advanced radio interface(s),” Report ITUR M.2134, December 2008.
4. Alliance, N. G. M. N. "Description of network slicing concept." NGMN 5G P 1 (2016): 1.
5. Yegnanarayana, Bayya. Artificial neural networks. PHI Learning Pvt. Ltd., 2009.
6. Han, Bin, and Hans D. Schotten. "Machine Learning for Network Slicing Resource Management: A Comprehensive Survey." (2020).
7. Sciancalepore, Vincenzo, Flavio Cirillo, and Xavier Costa-Perez. "Slice as a service (SlaaS) optimal IoT slice resources orchestration." GLOBECOM 2017-2017 IEEE Global Communications Conference. IEEE, 2017.
8. Caballero, Pablo, et al. "Network slicing for guaranteed rate services: Admission control and resource allocation games." IEEE Transactions on Wireless Communications 17.10 (2018): 6419-6432.
9. Kim, Juhoon, and Min Xie. "A study of slice-aware service assurance for network function virtualization." 2019 IEEE Conference on Network Softwarization (NetSoft). IEEE, 2019.
10. Vo, Phuong Luu, et al. "Slicing the edge: Resource allocation for RAN network slicing." IEEE Wireless Communications Letters 7.6 (2018): 970-973.
11. Jiang, Menglan, Massimo Condoluci, and Toktam Mahmoodi. "Network slicing in 5G: An auction-based model." 2017 IEEE International Conference on Communications (ICC). IEEE, 2017.
12. Samdanis, Konstantinos, and Xavier Costa Perez. "Network management system and method for a shared radio access network, RAN, infrastructure." U.S. Patent No. 10,506,471. 10 Dec. 2019.
13. Kammoun, Amal, et al. "End-to-end efficient heuristic algorithm for 5G network slicing." 2018 IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA). IEEE, 2018.
14. Caballero, Pablo, et al. "Network slicing games: Enabling customization in multi-tenant mobile networks." IEEE/ACM Transactions on Networking 27.2 (2019): 662-675.
15. Zhang, Li. "Proportional response dynamics in the Fisher market." Theoretical Computer Science 412.24 (2011): 2691-2698.
16. Kelly, Frank P., Aman K. Maulloo, and David KH Tan. "Rate control for communication networks: shadow prices, proportional fairness and stability." Journal of the Operational Research society 49.3 (1998): 237-252.
17. Johari, Ramesh, Shie Mannor, and John N. Tsitsiklis. "Efficiency loss in a network resource allocation game: the case of elastic supply." IEEE Transactions on Automatic Control 50.11 (2005): 1712-1724.
18. Yang, Sichao, and Bruce Hajek. "VCG-Kelly mechanisms for allocation of divisible goods: Adapting VCG mechanisms to one-dimensional signals." IEEE Journal on Selected Areas in Com. 25.6 (2007): 1237-1243.
19. Alliance, N. G. M. N. "Description of network slicing concept." NGMN 5G P 1.1 (2016).
20. 3GPP TR 23.799. "Study on Architecture for Next Generation System." (2016).
21. Caballero, Pablo, et al. "Network slicing for guaranteed rate services: Admission control and resource allocation games." IEEE Transactions on Wireless Communications 17.10 (2018): 6419-6432. [22] Bega, Dario, et al. "Optimising 5G infrastructure markets: The business of network slicing." IEEE INFOCOM 2017-IEEE Conf. on Computer Communications. IEEE, 2017.
22. Han, Bin, et al. "Admission and congestion control for 5G network slicing." 2018 IEEE Conference on Standards for Communications and Networking (CSCN). IEEE, 2018.
23. Li, Rongpeng, et al. "Deep reinforcement learning for resource management in network slicing." IEEE Access 6 (2018): 74429-74441.
24. Bega, Dario, et al. "DeepCog: Cognitive network management in sliced 5G networks with deep learning." IEEE INFOCOM 2019-IEEE Conference on Computer Communications. IEEE, 2019.
25. D’Oro, Salvatore, et al. "Auction-based resource allocation in OpenFlow multi-tenant networks." Computer Networks 115 (2017): 29-41.
26. Chang, Chia-Yu, Navid Nikaein, and Thrasyvoulos Spyropoulos. "Radio access network resource slicing for flexible service execution." IEEE INFOCOM 2018-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE, 2018.
27. Mo, Jeonghoon, and Jean Walrand. "Fair end-to-end window-based congestion control." IEEE/ACM Transactions on networking 8.5 (2000): 556-567.Available at http://www.topology-zoo.org/
28. 3GPP, “Evolved Universal Terrestrial Radio Access (E-UTRA); Physical layer procedures,” TS 36.213, v12.5.0, Rel. 12, Mar. 2015.
29. Thantharate, Anurag, and Cory Beard. "ADAPTIVE6G: Adaptive resource management for network slicing architectures in current 5G and future 6G systems." Journal of Net. and Systems Management 31, no. 1 (2023): 9.
30. Singh, Randeep, Abolfazl Mehbodniya, Julian L. Webber, Pankaj Dadheech, G. Pavithra, Mohammed S. Alzaidi, and Reynah Akwafo. "Analysis of network slicing for management of 5G networks using machine learning techniques." Wireless Communications and Mobile Computing 2022, no. 1 (2022): 9169568.
31. Alotaibi, Daifallah, Vijey Thayananthan, and Javad Yazdani. "The 5G network slicing using SDN based technology for managing network traffic." Procedia Computer Science 194 (2021): 114-121.
32. Saibharath, S., Sudeepta Mishra, and Chittaranjan Hota. "Joint QoS and energy-efficient resource allocation and scheduling in 5G Network Slicing." Computer Communications 202 (2023): 110-123.
33. Kim, Yohan, and Hyuk Lim. "Multi-agent reinforcement learning-based resource management for end-to-end network slicing." IEEE Access 9 (2021): 56178-56190.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
