Main Article Content
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
How to Cite
Detection of Abnormal Working Conditions, Principal Component Analysis, Logistic Regression, Application Virtualization Platforms, Container-based Virtualization, Real- time Abnormal Working Condition Detection, Preventive Maintenance
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