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


Erboy M., AKTAŞ M. S. , Tüzün H., Ünal E.

2020 5th International Conference on Computer Science and Engineering (UBMK), 09 September 2020 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/ubmk50275.2020.9219429
  • Keywords: Detection of Abnormal Working Conditions, Principal Component Analysis, Logistic Regression, Application Virtualization Platforms, Container-based Virtualization, Real-time Abnormal Working Condition Detection, Preventive Maintenance

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 he 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.