Fault-tolerance in Time Sensitive Network with Machine Learning Model

Nowadays, demand from the Internet of Things (IoT), automotive networking, and video applications is driving the transformation of Ethernet. It is a shift towards time-sensitive Ethernet. As a large amount of data is transmitted, many errors occur in the network. For this increased traffic, a Time Sensitive Network (TSN) is important. Time-Sensitive Network (TSN) is a technology that provides a definitive service for time sensitive traffic in an Ethernet environment that provides time-synchronization. In order to efficiently manage these errors, countermeasures against errors are required. A system that maintains its function even in the event of an internal fault or failure is called a Fault-Tolerant system. For this, after configuring the network environment using the OMNET++ program, machine learning was used to estimate the optimal alternative routing path in case an error occurred in transmission. By setting an alternate path before an error occurs, I propose a method to minimize delay and minimize data loss when an error occurs. Various methods were compared. First, when no replication environment and secondly when ideal replication, thirdly random replication, and lastly replication using ML were tested. In these experiments, replication in an ideal environment showed the best results, which is because everything is optimal. However, except for such an ideal environment, replication prediction using the suggested ML showed the best results. These results suggest that the proposed method is effective, but there may be problems with efficiency and error control, so an additional overview is provided for further improvement.