A Performance-Balanced Scheduling Algorithm for Diverse Real-World TSN Scenarios

Time-Sensitive Networking (TSN) achieves low-delay and low-jitter traffic transmission through different traffic scheduling mechanisms. However, despite numerous algorithms developed based on these mechanisms, most fail to concurrently support multipath, hybrid, and multicast traffic, which are prevalent in real-world scenarios. Moreover, balancing performance metrics such as success rate, bandwidth utilization, and computation overhead remains challenging for these algorithms, significantly limiting their application in diverse TSN scenarios. To solve this problem, this paper proposes a universal ultra-low-delay and zero-jitter traffic scheduling model. Based on this model, this paper further designs a performance-balanced algorithm. The algorithm improves traffic scheduling success rate through joint routing and scheduling, increases network bandwidth utilization through hybrid traffic scheduling, and achieves low computation overhead through policy-based searching. Finally, extensive experiments demonstrate that the algorithm effectively balances performance metrics across diverse real-world scenarios. It achieves high scheduling success rate under real-world traffic loads (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\gt $</tex-math></inline-formula>20% improvement over non-joint routing), increased bandwidth utilization in the presence of hybrid traffic (18.3% enhancement over non-hybrid traffic scheduling), and low computation overhead (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\lt $</tex-math></inline-formula> 2 minutes).

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Parallel and Distributed Systems
Published
2025-01-01
DOI
https://doi.org/10.1109/tpds.2025.3580402
Primary Topic
Cognitive Computing and Networks
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Performance-Balanced Scheduling Algorithm for Diverse Real-World TSN Scenarios

Tao Li, Wei Quan, Xuyan Jiang, Hui Yang et al.
IEEE Transactions on Parallel and Distributed Systems
Cognitive Computing and Networks
article

A Performance-Balanced Scheduling Algorithm for Diverse Real-World TSN Scenarios

Tao Li, Wei Quan, Xuyan Jiang, Hui Yang, Zhigang Sun, Qian Yang, Rulin Liu
article en

Abstract

Time-Sensitive Networking (TSN) achieves low-delay and low-jitter traffic transmission through different traffic scheduling mechanisms. However, despite numerous algorithms developed based on these mechanisms, most fail to concurrently support multipath, hybrid, and multicast traffic, which are prevalent in real-world scenarios. Moreover, balancing performance metrics such as success rate, bandwidth utilization, and computation overhead remains challenging for these algorithms, significantly limiting their application in diverse TSN scenarios. To solve this problem, this paper proposes a universal ultra-low-delay and zero-jitter traffic scheduling model. Based on this model, this paper further designs a performance-balanced algorithm. The algorithm improves traffic scheduling success rate through joint routing and scheduling, increases network bandwidth utilization through hybrid traffic scheduling, and achieves low computation overhead through policy-based searching. Finally, extensive experiments demonstrate that the algorithm effectively balances performance metrics across diverse real-world scenarios. It achieves high scheduling success rate under real-world traffic loads (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\gt $</tex-math></inline-formula>20% improvement over non-joint routing), increased bandwidth utilization in the presence of hybrid traffic (18.3% enhancement over non-hybrid traffic scheduling), and low computation overhead (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\lt $</tex-math></inline-formula> 2 minutes).

IEEE Transactions on Parallel and Distributed Systems
National University of Defense Technology (CN)
Openalex Percentile: Top 93%
Cognitive Computing and Networks
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.