Non-Periodic Traffic Allocation in Time-Aware TSN Networks

Time-sensitive Networking (TSN) is a set of protocols to ensure latency bounds and guarantees in 802-based networks. One of these protocols (Time-Aware Shaper, TAS) synchronizes the network with the arrival of periodic traffic to guarantee a free path from origin to destination for this type of traffic. However, non-periodic or randomly arriving traffic still needs to be transported under this synchronized network, making it challenging for the network engineer to find new time-aware (TAS) parameters. In this paper, we present three offline alternatives for finding these parameters at different levels of allocation rate, flow latency, and computational time. We modeled the problem as a network of polling systems, formulated and solved a mixed-integer linear program, and developed two allocation heuristics, all designed for offline computation. Lastly, we extensively evaluate the performance of all the alternative approaches.

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Publication Details

Journal
IEEE Access
Published
2025-01-01
DOI
https://doi.org/10.1109/access.2025.3627206
Primary Topic
Network Time Synchronization Technologies
Type
article
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article

Non-Periodic Traffic Allocation in Time-Aware TSN Networks

Neil Diaz, Natarajan Gautam
IEEE Access
Network Time Synchronization Technologies
article

Non-Periodic Traffic Allocation in Time-Aware TSN Networks

Neil Diaz, Natarajan Gautam
article en

Abstract

Time-sensitive Networking (TSN) is a set of protocols to ensure latency bounds and guarantees in 802-based networks. One of these protocols (Time-Aware Shaper, TAS) synchronizes the network with the arrival of periodic traffic to guarantee a free path from origin to destination for this type of traffic. However, non-periodic or randomly arriving traffic still needs to be transported under this synchronized network, making it challenging for the network engineer to find new time-aware (TAS) parameters. In this paper, we present three offline alternatives for finding these parameters at different levels of allocation rate, flow latency, and computational time. We modeled the problem as a network of polling systems, formulated and solved a mixed-integer linear program, and developed two allocation heuristics, all designed for offline computation. Lastly, we extensively evaluate the performance of all the alternative approaches.

IEEE AccessVol. 13
Syracuse University (US), Texas A&M University (US)
Openalex Percentile: Top 63%
Network Time Synchronization Technologies
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