Comparative Analysis of Network Fault Classification Using Machine Learning

The recent evolution in network technologies such as network function virtualization (NFV) and cloud-native network function (CNF) is expected to enable the delivery of on-demand network services to meet various requirements such as reliability and latency. However, the operation of networks using these emerging technologies can be complex due to the need for a greater number of network entities and more types of service level agreements (SLAs). This incurs a high operational workload. On the other hand, there have been major advances in machine learning (ML) in this decade. These have made it possible for many and diverse industries to leverage ML capabilities to support human tasks. In this paper, we apply ML to analyze the root cause of failures in an NFV environment. To identify the appropriate approach, we analyze the performance of an ML-based fault classifier from several perspectives, three algorithms [multi-layer perceptron (MLP), random forest (RF), support vector machine (SVM)], volume and balance of training data, and number of features. The experiment on the testbed using our ML comparison framework shows that RF provides the highest performance even with a small amount of data, and also that SVM could improve its performance by increasing training data, feature reduction, or balance adjustment of normal/abnormal samples.