Enhancing Fault Detection in Time Sensitive Networks using Machine Learning

被引：0

作者：

Desai, Nitin ^{[1
]}

Punnekkat, Sasikumar ^{[1
]}

机构：

[1] Malardalen Univ, Vasteras, Sweden

来源：

2020 INTERNATIONAL CONFERENCE ON COMMUNICATION SYSTEMS & NETWORKS (COMSNETS) | 2020年

关键词：

Time sensitive networking; network configuration; machine learning; safety-critical systems; fault-tolerance; redundancy; fault-detection;

D O I：

10.1109/comsnets48256.2020.9027357

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Time sensitive networking (TSN) is gaining attention in industrial automation networks since it brings essential real-time capabilities to the Ethernet layer. Safety-critical real-time applications based on TSN require both timeliness as well as fault-tolerance guarantees. The TSN standard 802. 1CB introduces seamless redundancy mechanisms for time-sensitive data whereby each data frame is sequenced and duplicated across a redundant link to prevent single points of failure (most commonly, link failures). However, a major shortcoming of 802. 1CB is the lack of fault detection mechanisms which can result in unnecessary replications even under good link conditions - clearly inefficient in terms of bandwidth use. This paper proposes a machine learning-based intelligent configuration synthesis mechanism that enhances bandwidth utilization by replicating frames only when a link has a higher propensity for failure.

引用

页数：6

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