Virtual machine abnormal behavior detection is an effective way to help cloud platform administrators monitor the running status of cloud platform to improve the reliability of cloud platform, which has become one of the research hotspots in the field of cloud computing. Aiming at the problems of high computational complexity and high false alarm rate in the existing virtual machine anomaly monitoring mechanism of cloud platform, this paper proposed a two-stage virtual machine abnormal behavior-based detection mechanism. Firstly, a workload-based incremental clustering algorithm is used to monitor and analyze both the virtual machine workload information and performance index information. Then, an online anomaly detection mechanism based on the incremental local outlier factor algorithm is designed to enhance detection efficiency. By applying this two-phase detection mechanism, it can significantly reduce the computational complexity and meet the needs of real-time performance. The experimental results are verified on the mainstream Openstack cloud platform.
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