Virtual machines (VM) on a Cloud platform can be influenced by a variety of factors which can lead to decreased performance and downtime, affecting the reliability of the Cloud platform. Traditional anomaly detection algorithms and strategies for Cloud platforms have some flaws in their accuracy of detection, detection speed, and adaptability. In this paper, a dynamic and adaptive anomaly detection algorithm based on Self-Organizing Maps (SOM) for virtual machines is proposed. A unified modeling method based on SOM to detect the machine performance within the detection region is presented, which avoids the cost of modeling a single virtual machine and enhances the detection speed and reliability of large-scale virtual machines in Cloud platform. The important parameters that affect the modeling speed are optimized in the SOM process to significantly improve the accuracy of the SOM modeling and therefore the anomaly detection accuracy of the virtual machine.
Liu et al. REF proposed a dynamic and adaptive anomaly detection method base on self-organizing maps, where SOM network method was used to model the set of virtual machines with some similar running properties to reduce the modeling time and the training cost.
An Anomaly Detection Algorithm of Cloud Platform Based on Self-Organizing Maps
Mathematical Problems in Engineering
Mathematics