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Analysis of Latency-Aware Network Slicing in 5G Packet xHaul Networks

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Packet-switched xHaul networks are a scalable solution enabling convergent transport of diverse types of radio data flows, such as fronthaul / midhaul / backhaul (FH / MH / BH) flows, between remote sites and a central site (hub) in 5G radio access networks (RANs). Such networks can be realized using the cost-efficient Ethernet technology, which enhanced with time-sensitive networking (TSN) features allows for prioritized transmission of latency-sensitive fronthaul flows. Provisioning of multiple types of 5G services of different service requirements in a shared network, commonly referred to as network slicing, requires adequate handling of transported data flows in order to satisfy particular service / slice requirements. In this work, we investigate two traffic prioritization policies, namely, flowaware (FA) and latency-aware (LA), in a packet-switched xHaul network supporting slices of different latency requirements. We evaluate the effectiveness of the policies in a networkplanning case study, where virtualized radio processing resources allocated at the processing pool (PP) facilities, for two slices related to enhanced mobile broadband (eMBB) and ultra-reliable low latency communications (URLLC) services, are subject to optimization. Using numerical experiments, we analyze PP cost savings from applying the LA policy (vs. FA) in various network scenarios. The savings in active PPs reach up to 40% − 60% in ring scenarios and 30% in a mesh network, whereas the gains in overall PP cost are up to 20% for the cost values assumed in the analysis.
Rocznik
Strony
335--340
Opis fizyczny
Bibliogr. 20 poz., rys. tab., wykr.
Twórcy
  • National Institute of Telecommunications, Warsaw, Poland
Bibliografia
  • [1] 3GPP, “Study on new radio access technology: Radio access architecture and interfaces,” Tech. Rep. TR 38.801, v14.0.0, Sophia Antipolis, France, 2017.
  • [2] --, “Architecture description (release 17),” Tech. Spec. TS 38.401, v17.0.0, Sophia Antipolis, France, 2022.
  • [3] Y. Xiao, J. Zhang, and Y. Ji, “Can fine-grained functional split benefit to the converged optical-wireless access networks in 5G and beyond?” IEEE Trans. Netw. Serv. Manag., vol. 17, no. 3, pp. 1774-1787, 2020. [Online]. Available: https://doi.org/10.1109/TNSM.2020.2995844
  • [4] IEEE, “IEEE standard for packet-based fronthaul transport networks,” https://standards.ieee.org/project/1914 1.html, (accessed on 28 September 2020). [Online]. Available: https://standards.ieee.org/project/1914 1.html
  • [5] J. Ordonez-Lucena et al., “Network slicing for 5G with SDN/NFV: Concepts, architectures, and challenges,” IEEE Comm. Mag., vol. 55, no. 5, pp. 80-87, 2017. [Online]. Available: https://doi.org/10.1109/MCOM.2017.1600935
  • [6] S. Vassilaras et al., “The algorithmic aspects of network slicing,” IEEE Comm. Mag., vol. 55, no. 8, pp. 112-119, 2017. [Online]. Available: https://doi.org/10.1109/MCOM.2017.1600939
  • [7] IEEE, “802.1cm-2018 - IEEE standard for local and metropolitan area networks - time-sensitive networking for fronthaul,” Nov. 2018.
  • [8] “Common public radio interface: eCPRI V2.0 interface specification,” 10 May 2019.
  • [9] G. O. Perez, D. Larrabeiti, and J. A. Hernandez, “5G new radio fronthaul network design for eCPRI-IEEE 802.1CM and extreme latency percentiles,” IEEE Access, vol. 7, pp. 82 218-82 229, 2019. [Online]. Available: https://doi.org/10.1109/ACCESS.2019.2923020
  • [10] A. Esmaeily, K. Kralevska, and T. Mahmoodi, Slicing Scheduling for Supporting Critical Traffic in Beyond 5G. IEEE, Jan. 2022.
  • [11] J. Yusupov, A. Ksentini, G. Marchetto, and R. Sisto, “Multi-objective function splitting and placement of network slices in 5g mobile networks,” in Proc. of IEEE CSCN, Paris, France, Oct. 2018. [Online]. Available: https://doi.org/10.1109/CSCN.2018.8581714
  • [12] S. Bhattacharjee et al., “Network slicing for TSN-based transport networks,” IEEE Access, vol. 9, pp. 62 788–62 809, 2021. [Online]. Available: https://doi.org/10.1109/ACCESS.2021.3074802
  • [13] M. Klinkowski, “Optimization of latency-aware flow allocation in NGFI networks,” Comp. Commun., vol. 161, pp. 344–359, 2020. [Online]. Available: https://doi.org/10.1016/j.comcom.2020.07.044
  • [14] -- “Latency-aware DU/CU placement in convergent packet-based 5G fronthaul transport networks,” Appl. Sci., vol. 10, no. 21, 2020.
  • [15] M. A. Imran, S. A. R. Zaidi, and M. Z. Shakir, Access, Fronthaul and Backhaul Networks for 5G & Beyond. Institution of Engineering and Technology, 2017. [Online]. Available: https://doi.org/10.1109/MCOM.2017.1600735
  • [16] H. Yu, F. Musumeci, J. Zhang, Y. Xiao, M. Tornatore, and Y. Ji, “DU/CU placement for C-RAN over optical metro-aggregation networks,” in Proc. of ONDM, Athens, Greece, May 2019. [Online]. Available: https://doi.org/10.1007/978-3-030-38085-4 8
  • [17] ITU-T Technical Report, “Transport network support of IMT-2020/5G,” Oct. 2018.
  • [18] B. M. Khorsandi and C. Raffaelli, “BBU location algorithms for survivable 5G C-RAN over WDM,” Comput. Netw., vol. 144, pp. 53-63, 2018. [Online]. Available: https://doi.org/10.1016/j.comnet.2018.07.026
  • [19] S. Lagen, L. Giupponi, A. Hansson, and X. Gelabert, “Modulation compression in next generation RAN: Air interface and fronthaul tradeoffs,” IEEE Comm. Mag., vol. 59, no. 1, pp. 89-95, 2021.
  • [20] IBM, “CPLEX optimizer,” http://www.ibm.com/, (accessed on 30 September 2022). [Online]. Available: http://www.ibm.com/
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-e7af845f-7297-4275-a106-64b259ccdc1e
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