Dynamic fault diagnosis framework for virtual machine rolling upgrade operation in google cloud platform

Abstract

Now a day's failure of system outages in cloud application is the main drawbacks of cloud environment. It reduces the economic losses for the business environment. Anomaly detection in the cloud system will reduce the loss. Anomaly detection at the user end is difficult, particularly Rolling Upgrade operation. Due to the enormous anomaly the operations are indistinguishable. It is very difficult to find the faults or anomalies. Current system fails to provide reliable assurance of successful execution to the system. Proposed anomaly detection using Decision tree J48 classifier giving the reliable assurance during running virtual machine upgrade operation. The proposed method was evaluated on the Google Cloud Platform. It will give the high precision, recall, accuracy to the cloud environment.