Machine Learning Techniques for Fault Tolerance Management

Many businesses are rapidly adopting new digital technology to boost productivity. They need to incorporate cutting-edge digital advances like mobility, data analytics, the cloud, and the Internet of Things (IoT) into their processes and IT systems to stay competitive. Thanks to automation, deploying a network now takes just a few seconds. This has brought up some new issues with the setup or the blueprint. A fault-tolerant system provides high levels of timeliness and accuracy to resolve these routing issues. Across this section, we’ll look at fault tolerance issues in various networks, from WANs to MANs. To address the issue of fault tolerance in scheduled jobs, we present a strategy based on machine learning techniques like K-Nearest Neighbor (KNN) and support vector machine (SVM). By analyzing the jobs’ energy radius and distance, we could apply the KNN method to determine when a failing cluster’s jobs were reset. The SVM is used to categorize the fault tolerance tasks as a hybrid, core, and single node to analyze the job failure at various levels with the different nodes. All these findings validate the usefulness of the suggested method for the fault tolerance study of network routers.