Market Analysis of MEC-Assisted Beyond 5G Ecosystem

被引:20
作者
Nakazato, Jin [1 ]
Nakamura, Makoto [1 ]
Yu, Tao [1 ]
Li, Zongdian [1 ]
Maruta, Kazuki [2 ]
Tran, Gia Khanh [1 ]
Sakaguchi, Kei [1 ,2 ]
机构
[1] Tokyo Inst Technol, Dept Elect & Elect Engn, Tokyo 1528552, Japan
[2] Tokyo Inst Technol, Acad Super Smart Soc, Tokyo 1528550, Japan
基金
欧盟地平线“2020”;
关键词
Telecommunications; 5G mobile communication; Edge computing; Cloud computing; Ecosystems; Computational modeling; Cellular networks; Mobile edge computing; multi-access edge computing; telecom operator; cloud ownercess edge computing; 5G and beyond; heterogeneous cellular networks; ecosystem; cloud owner; revenue; CAPEX; OPEX; SERVICE MIGRATION; EDGE; MOBILE; NETWORKS; SCENARIOS;
D O I
10.1109/ACCESS.2021.3068839
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The quality-of-service (QoS)/quality-of-experience (QoE) demands of mobile services are soaring and have overwhelmed the obsolescent capability of 3G and 4G cellular networks. The emerging 5G networks will bring an unprecedented promotion in transmission data rates. However, the satisfaction of some service requirements is still in dilemma, especially the end-to-end (E2E) latency which varies in different applications. Multi-access edge computing (MEC), a promising technology in 5G cellular networks, can provide ultra-low E2E latency and reduce traffic load on mobile backhaul networks. The potential benefits of MEC for 5G and beyond services have been explored by preliminary studies. What remains is the uncertainty of revenue from the investment of MEC which will shake operators' decisions about whether and how to deploy MEC in cellular networks. In this light, this paper designs a MEC-assisted 5G and beyond ecosystem inclusive of three players: private (local) telecom operators, backhaul, and cloud service owners. We propose a revenue maximization model for private (local) telecom operators and cloud service owners to minimize the cost from the end-user perspective while satisfying the latency requirement. The derived model indicates that two players' revenues can be maximized by optimizing MEC resources and backhaul capacity. The game-theoretic analyses also reveal the optimized hybrid strategy of MEC and cloud for efficient mobile traffic management.
引用
收藏
页码:53996 / 54008
页数:13
相关论文
共 72 条
  • [1] Mobile Edge Computing: A Survey
    Abbas, Nasir
    Zhang, Yan
    Taherkordi, Amir
    Skeie, Tor
    [J]. IEEE INTERNET OF THINGS JOURNAL, 2018, 5 (01): : 450 - 465
  • [2] Next Generation 5G Wireless Networks: A Comprehensive Survey
    Agiwal, Mamta
    Roy, Abhishek
    Saxena, Navrati
    [J]. IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2016, 18 (03): : 1617 - 1655
  • [3] Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications
    Al-Fuqaha, Ala
    Guizani, Mohsen
    Mohammadi, Mehdi
    Aledhari, Mohammed
    Ayyash, Moussa
    [J]. IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2015, 17 (04): : 2347 - 2376
  • [4] [Anonymous], 2017, 2017 INT C WIRELESS, DOI [DOI 10.1109/WINCOM.2017.8238174, 10.1109/WINCOM.2017.8238174]
  • [5] [Anonymous], 2018, MEC ENT SETT SOL OUT
  • [6] [Anonymous], 2019, MILLIM WAVE EDGE CLO
  • [7] [Anonymous], 2016, MMMAGIC21
  • [8] [Anonymous], 2015, MOBILE EDGE COMPUTIN
  • [9] Living on the Edge: The Role of Proactive Caching in 5G Wireless Networks
    Bastug, Ejder
    Bennis, Mehdi
    Debbah, Merouane
    [J]. IEEE COMMUNICATIONS MAGAZINE, 2014, 52 (08) : 82 - 89
  • [10] Business Application Acquisition: On-Premise or SaaS-Based Solutions?
    Bibi, Stamatia
    Katsaros, Dimitrios
    Bozanis, Panayiotis
    [J]. IEEE SOFTWARE, 2012, 29 (03) : 86 - 93