Virtual Machine Failure Prediction using Log Analysis

In this study, we propose a machine learning model that predicts failures by analyzing logs before failures occur in virtual machines (VMs) used in network function virtualization (NFV) environments. The proposed model utilizes convolutional neural network (CNN) and includes pre-processing and pre-failure tagging techniques. We collected log data from VMs built on OpenStack to validate the proposed model. We classified failures based on early fault messages and built a CNN model to predict VM failures. The experimental results showed that the proposed model can predict failures before 5 minutes with the F1 score of 0.67. The proposed model will be used for VM proactive live migration to avoid service degradation and interruptions caused by failures.