Network Calculus Results for TSN: An Introduction
August 26, 2025 Β· Declared Dead Β· π Information and Communication Technology Convergence
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Lisa Maile, Kai-Steffen Hielscher, Reinhard German
arXiv ID
2508.18855
Category
cs.NI: Networking & Internet
Citations
45
Venue
Information and Communication Technology Convergence
Last Checked
6 months ago
Abstract
Time-Sensitive Networking (TSN) is a set of standards that enables the industry to provide real-time guarantees for time-critical communications with Ethernet hardware. TSN supports various queuing and scheduling mechanisms and allows the integration of multiple traffic types in a single network. Network Calculus (NC) can be used to calculate upper bounds for latencies and buffer sizes within these networks, for example, for safety or real-time traffic. We explain the relevance of NC for TSN-based computer communications and potential areas of application. Different NC analysis approaches have been published to examine different parts of TSN and this paper provides a survey of these publications and presents their main results, dependencies, and differences. We present a consistent presentation of the most important results and suggest an improvement to model the output of sending end-devices. To ease access to the current research status, we introduce a common notation to show how all results depend on each other and also identify common assumptions. Thus, we offer a comprehensive overview of NC for industrial networks and identify possible areas for future work.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Networking & Internet
R.I.P.
π»
Ghosted
π
π
The Cartographer
Federated Learning in Mobile Edge Networks: A Comprehensive Survey
π
π
The Cartographer
A Survey of Indoor Localization Systems and Technologies
R.I.P.
π»
Ghosted
Survey of Important Issues in UAV Communication Networks
π
π
The Cartographer
Network Function Virtualization: State-of-the-art and Research Challenges
π
π
The Cartographer
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted