<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Time-Sensitive Networking (TSN) occupies a vital position in modern communication domains, with the 802.1Qbv standard being an important network technology designed to meet real-time requirements. This standard requires network traffic to be transmitted within strict time windows, presenting challenges in network planning, necessitating efficient resource allocation and scheduling strategies. This paper addresses the 802.1Qbv planning problem through the introduction of reinforcement learning algorithms, offering an automated and intelligent solution. We have designed a reinforcement learning agent capable of perceiving network status, learning optimal resource allocation strategies, and dynamically adjusting in real-time environments. Through simulation and experimentation, we have validated the effectiveness of our proposed method, comparing it with traditional planning approaches. The contribution of this study lies in introducing a novel solution to the 802.1Qbv planning problem for time-sensitive networks, enhancing network resource utilization and performance. This approach offers strong support for the development and enhancement of TSN-like networks, holding significant importance for meeting the growing demands of real-time applications.</p>
</abstract>

Time-Sensitive Networking (TSN) occupies a vital position in modern communication domains, with the 802.1Qbv standard being an important network technology designed to meet real-time requirements. This standard requires network traffic to be transmitted within strict time windows, presenting challenges in network planning, necessitating efficient resource allocation and scheduling strategies. This paper addresses the 802.1Qbv planning problem through the introduction of reinforcement learning algorithms, offering an automated and intelligent solution. We have designed a reinforcement learning agent capable of perceiving network status, learning optimal resource allocation strategies, and dynamically adjusting in real-time environments. Through simulation and experimentation, we have validated the effectiveness of our proposed method, comparing it with traditional planning approaches. The contribution of this study lies in introducing a novel solution to the 802.1Qbv planning problem for time-sensitive networks, enhancing network resource utilization and performance. This approach offers strong support for the development and enhancement of TSN-like networks, holding significant importance for meeting the growing demands of real-time applications.

The Time-Sensitive Networking Scheduling Algorithm Based on Q-learning

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"The Time-Sensitive Networking Scheduling Algorithm Based on Q-learning"}

Time-Sensitive Networking (TSN) occupies a vital position in modern communication domains, with the 802.1Qbv standard being an important network technology...

The Time-Sensitive Networking Scheduling Algorithm Based on Q-learning

Online veröffentlicht: 28. März 2024

Seitenbereich: 78 - 86

DOI: https://doi.org/10.2478/ijanmc-2024-0008

Schlüsselwörter
Time-Sensitive Networking, Reinforcement Learning, Network Planning, IEEE 802.1Qbv

© 2024 Jiayi Zhao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The Time-Sensitive Networking Scheduling Algorithm Based on Q-learning

Online veröffentlicht: 28. März 2024

Seitenbereich: 78 - 86

DOI: https://doi.org/10.2478/ijanmc-2024-0008

SchlüsselwörterTime-Sensitive Networking, Reinforcement Learning, Network Planning, IEEE 802.1Qbv

© 2024 Jiayi Zhao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Time-Sensitive Networking, Reinforcement Learning, Network Planning, IEEE 802.1Qbv