Real-Time Detection of Anomalies on Performance Data of Container Virtualization Platforms

Abstract

Application virtualization platforms are virtualization technologies that allow applications to run independently. It is observed that applications running on application virtualization platforms may have abnormal working conditions from time to time. However, such situations can be caught by system administrators examining the application log files in detail. This causes abnormal operating conditions to be captured long after they occur. Within the scope of this research, a method that allows to detect abnormal running conditions of applications running on application virtualization platforms in real time is proposed. The proposed method uses both unsupervised learning and supervised learning algorithms together. A prototype application was developed to demonstrate the usability of the proposed method. In order to demonstrate the success of the method, the tests we performed on the prototype yielded high accuracy in a real-time detection of abnormal operating conditions.