云虚拟机异常检测场景下改进的LOF算法

针对云服务中由于资源超额预定造成负载不均衡的云虚拟机异常，提出了一种基于密度空间的局部离群因子（Local Outlier Factor Based on Density Space，LOFBDS）算法。LOFBDS算法参考DBSCAN（Density-Based Spatial Clustering of Applications with Noise）算法，将云虚拟机在密度空间中的性质融合至LOF算法之中，提出对云虚拟机的判断规则，以达到优化对正常云虚拟机的检测过程，提高检测效率。实验结果表明，所提出的算法对云服务负载不均造成的云虚拟机异常有着良好的检测效率，并且时间花费较少。

Improved LOF Algorithm in Cloud Virtual Machine Anomaly Detection Scenario

In this paper, a LOFBDS（Local Outlier Factor Based on Density Space） algorithm is proposed to detect the cloud virtual machine anomaly with unbalanced load caused by resource overbooking in cloud services. The LOFBDS algorithm refers to the DBSCAN（Density Based Spatial Clustering of Applications with Noise） algorithm. It integrates the properties of cloud virtual machines in the density space into the LOF algorithm, and proposes judgment rules for cloud virtual machines to optimize the normal cloud virtual machine to improve detection efficiency. Experimental results show that the algorithm proposed in this paper has a good detection efficiency and less time cost for cloud virtual machine exceptions caused by uneven cloud service load.