Research on traffic scheduling algorithm and parameter optimization based on load balancing in time-sensitive network

Zhao Chen

doi:10.1117/12.3029078

22 May 2024 Research on traffic scheduling algorithm and parameter optimization based on load balancing in time-sensitive network

Zhao Chen

Author Affiliations +

Proceedings Volume 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023); 131762E (2024) https://doi.org/10.1117/12.3029078
Event: Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 2023, Hangzhou, China

Abstract

Time-sensitive network (TSN) guarantees deterministic delivery of traffic by prioritizing it differently. Commonly used traffic scheduling algorithm is time-aware shaper (TAS) combined with credit-based shaper (CBS). Aiming at the problem of excessive reserved bandwidth for stream reservation (SR) traffic during CBS scheduling, a traffic scheduling strategy based on load balancing was proposed. And the reserved bandwidth parameters of the SR traffic were optimized after completing the modeling of its delay problem. This strategy sliced and encapsulated the SR traffic packets into equal-length data frames at the end nodes and made them uniformly transmitted during the sending cycle. It reduced the end-to-end worst response time of the SR traffic due to packet congestion. After that the delay model was solved using simulated annealing algorithm to get the optimal reserved bandwidth. Simulation results showed that the proposed SR traffic scheduling strategy effectively reduced the reserved bandwidth and the delay of traffic transmission inside the switch on the basis of satisfying the traffic schedulability.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhao Chen "Research on traffic scheduling algorithm and parameter optimization based on load balancing in time-sensitive network", Proc. SPIE 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 131762E (22 May 2024); https://doi.org/10.1117/12.3029078

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KEYWORDS

Data transmission

Switches

Algorithms

Mathematical optimization

Packet switching

Performance modeling

Data modeling

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