A Docker Container Anomaly Monitoring System Based on Optimized Isolation Forest


Virtualization that is based on containers has gradually developed into a primary solution in the Cloud Computing environments of today. A significant obstacle for cloud vendors and their customers is the detection and investigation of anomalies within containers. The purpose of this paper is to propose an online container anomaly detection system. This system will monitor and analyze multidimensional resource metrics of containers using an optimized isolation forest algorithm as its foundation. In order to improve the accuracy of the detection, it gives a certain amount of weight to each resource metric and modifies the isolation forest algorithm so that it uses weighted feature selection rather than random feature selection. This is done in accordance with the resource bias of the container. In addition to this, it has the ability to recognize abnormal resource metrics and automatically adjust the monitoring period in order to cut down on monitoring delay and system overhead. In addition to this, it is able to determine the reasons for the anomalies by examining and investigating the container log. The results of the experiments show that the system is capable of performing well and is efficient when it comes to detecting the typical irregularities that occur in containers in both simulated and actual cloud environments.

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