Network slicing resource allocation algorithm based on bipartite graph matching in smart grids

被引：0
|
作者：
Xia W. [1 ]
Xin Y. [1 ]
Liang D. [2 ]
Wu J. [2 ]
Wang X. [3 ]
Yan F. [1 ]
Shen L. [1 ]
机构：
[1] National Mobile Communications Research Laboratory, Southeast University, Nanjing
[2] State Grid Shandong Electric Power Company Jinan Power Supply Company, Jinan
[3] Department of Electrical and Computer Engineering, The State University of New York, Stony Brook, 11794, NY
来源：
Tongxin Xuebao/Journal on Communications | 2024年 / 45卷 / 03期
关键词：
auction; bipartite graph matching; network slicing; resource allocation; smart grid;
D O I：
10.11959/j.issn.1000-436x.2024055
中图分类号：
学科分类号：
摘要：
To solve the problem of simultaneously satisfying the quality of service requirements of multiple types of services in smart grids and considering the economic utility of power terminals and network side, a network slicing resource allocation algorithm based on bipartite graph matching was proposed. For the control and collection services in smart grids, the corresponding bidding information was formulated for the power terminals, and the payment price and utility matrix were calculated accordingly. The resource allocation between the network slices and the power terminals was modeled as a bipartite graph matching problem. Different slicing resources were allocated to the terminals according to the latency, transmission rate, or energy consumption requirements of different services to maximize the system utility. Simulation results show that the proposed algorithm is able to improve the system utility by 10%~20% compared to the existing double auction algorithm and greedy algorithm. © 2024 Editorial Board of Journal on Communications. All rights reserved.
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页码：17 / 28
页数：11
相关论文
共 23 条
  • [1] FOUKAS X, PATOUNAS G, ELMOKASHFI A, Et al., Network slicing in 5G: survey and challenges, IEEE Communications Magazine, 55, 5, pp. 94-100, (2017)
  • [2] ROST P, MANNWEILER C, MICHALOPOULOS D S, Et al., Network slicing to enable scalability and flexibility in 5G mobile networks, IEEE Communications Magazine, 55, 5, pp. 72-79, (2017)
  • [3] ZHONG L J, HU J B, SHEN H C, Et al., Slice allocation of 5G network for smart grid with deep reinforcement learning ACKTR, Proceedings of the 2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP), pp. 242-249, (2022)
  • [4] RICHART M, BALIOSIAN J, SERRAT J, Et al., Resource slicing in virtual wireless networks: a survey, IEEE Transactions on Network and Service Management, 13, 3, pp. 462-476, (2016)
  • [5] ZHOU X, LI R P, CHEN T, Et al., Network slicing as a service: enabling enterprises’ own software-defined cellular networks, IEEE Communications Magazine, 54, 7, pp. 146-153, (2016)
  • [6] VASSILARAS S, GKATZIKIS L, LIAKOPOULOS N, Et al., The algorithmic aspects of network slicing, IEEE Communications Magazine, 55, 8, pp. 112-119, (2017)
  • [7] LIANG L, WU Y F, FENG G, Et al., Online auction-based resource allocation for service-oriented network slicing, IEEE Transactions on Vehicular Technology, 68, 8, pp. 8063-8074, (2019)
  • [8] WANG Z Y, WEI Y F, YU F R, Et al., Utility optimization for resource allocation in multi-access edge network slicing: a twin-actor deep deterministic policy gradient approach, IEEE Transactions on Wireless Communications, 21, 8, pp. 5842-5856, (2022)
  • [9] HE N, YANG S, LI F, Et al., Intimacy-based resource allocation for network slicing in 5G via deep reinforcement learning, IEEE Network, 35, 6, pp. 111-118, (2021)
  • [10] YANG X L, YAO T, XIN R, Et al., Slicing allocation optimization based on MEC service-aware in 5G smart grids enabled surveillance system, Proceedings of the 2021 7th International Conference on Computer and Communications (ICCC), pp. 2269-2273, (2021)
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