Optimization of Flow Allocation in Asynchronous Deterministic 5G Transport Networks by Leveraging Data Analytics Prados Garzón, Jonathan Taleb, Tarik Bagaa, Miloud Transport Networks QoS Performance guarantees Flow Allocation Time-Sensitive Networking (TSN) 5G Data analytics Asynchronous Traffic Shaper (ATS) IEEE 802.1Qcr This research work was supported in part by the Euro- pean Union’s Horizon 2020 Research and Innovation Program under the “Cloud for Holography and Augmented Reality (CHARITY)” Project under Agreement 101016509, and 5G- CLARITY Project under Agreement 871428. It is also partially supported by the Spanish national research project TRUE5G: PID2019-108713RB-C53. Time-Sensitive Networking (TSN) and Deterministic Networking (DetNet) technologies are increasingly recognized as key levers of the future 5G transport networks (TNs) due to their capabilities for providing deterministic Quality-of-Service and enabling the coexistence of critical and best-effort services. Addi- tionally, they rely on programmable and cost-effective Ethernet- based forwarding planes. This article addresses the flow alloca- tion problem in 5G backhaul networks realized as asynchronous TSN networks, whose building block is the Asynchronous Traffic Shaper. We propose an offline solution, dubbed “Next Generation Transport Network Optimizer” (NEPTUNO), that combines ex- act optimization methods and heuristic techniques and leverages data analytics to solve the flow allocation problem. NEPTUNO aims to maximize the flow acceptance ratio while guaranteeing the deterministic Quality-of-Service requirements of the critical flows. We carried out a performance evaluation of NEPTUNO regarding the degree of optimality, execution time, and flow rejection ratio. Furthermore, we compare NEPTUNO with a novel online baseline solution for two different optimization goals. Online methods compute the flow’s allocation configuration right after the flow arrives at the network, whereas offline solutions like NEPTUNO compute a long-term configuration allocation for the whole network. Our results highlight the potential of data analytics for the self-optimization of the future 5G TNs. 2023-05-23T11:03:36Z 2023-05-23T11:03:36Z 2023-03-01 journal article Published version: J. Prados-Garzon, T. Taleb and M. Bagaa, "Optimization of Flow Allocation in Asynchronous Deterministic 5G Transport Networks by Leveraging Data Analytics," in IEEE Transactions on Mobile Computing, vol. 22, no. 3, pp. 1672-1687, 1 March 2023, doi: 10.1109/TMC.2021.3099979 https://hdl.handle.net/10481/81764 10.1109/TMC.2021.3099979 eng info:eu-repo/grantAgreement/EC/H2020/101016509 http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional IEEE Computer Soc