University of Oulu

J. Prados-Garzon and T. Taleb, "Asynchronous Time-Sensitive Networking for 5G Backhauling," in IEEE Network, vol. 35, no. 2, pp. 144-151, March/April 2021, doi: 10.1109/MNET.011.2000402

Asynchronous time-sensitive networking for 5G backhauling

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Author: Prados-Garzon, Jonathan1; Taleb, Tarik1,2,3
Organizations: 1Department of Communications and Networking, Aalto University, Finland
2Faculty of Information Technology and Electrical Engineering, University of Oulu, Finland
3Department of Computer and Information Security, Sejong University, Seoul 05006, South Korea
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 3.2 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2021053132257
Language: English
Published: Institute of Electrical and Electronics Engineers, 2021
Publish Date: 2021-05-31
Description:

Abstract

Fifth Generation (5G) phase 2 rollouts are around the corner to make mobile ultra-reliable and low-latency services a reality. However, to realize that scenario, besides the new 5G built-in Ultra-Reliable Low-Latency Communication (URLLC) capabilities, it is required to provide a substrate network with deterministic Qual-ity-of-Service support for interconnecting the different 5G network functions and services. Time-Sensitive Networking (TSN) appears as an appealing network technology to meet the 5G connectivity needs in many scenarios involving critical services and their coexistence with Mobile Broadband traffic. In this article, we delve into the adoption of asynchronous TSN for 5G backhauling and some of the relevant related aspects. We start motivating TSN and introducing its mainstays. Then, we provide a comprehensive overview of the architecture and operation of the Asynchronous Traffic Shaper (ATS), the building block of asynchronous TSN. Next, a management framework based on ETSI Zero-touch network and Service Management (ZSM) and Abstraction and Control of Traffic Engineered Networks (ACTN) reference models is presented for enabling the TSN transport network slicing and its interworking with Fifth Generation (5G) for backhauling. Then we cover the flow allocation problem in asynchronous TSNs and the importance of Machine Learning techniques for assisting it. Last, we present a simulation-based proof-of-concept (PoC) to assess the capacity of ATS-based forwarding planes for accommodating 5G data flows.

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Series: IEEE network
ISSN: 0890-8044
ISSN-E: 1558-156X
ISSN-L: 0890-8044
Volume: 35
Issue: 2
Pages: 144 - 151
DOI: 10.1109/MNET.011.2000402
OADOI: https://oadoi.org/10.1109/MNET.011.2000402
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
Funding: This work is partially supported by the European Union's Horizon 2020 research and innovation program under the CHARITY project with grant agreement No. 101016509. It is also partially supported by the Academy of Finland Project CSN, under Grant Agreement 311654 and the 6Genesis project under Grant No. 318927.
Academy of Finland Grant Number: 311654
318927
Detailed Information: 311654 (Academy of Finland Funding decision)
318927 (Academy of Finland Funding decision)
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