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Aiming at high computation time in the process of cloud virtual machine anomaly detection, a C-t-SNE algorithm suitable for cloud is proposed. The algorithm classifies the virtual machines according to the different tasks, constructs the projection space according to the relevant parameters of the cloud virtual machines. Before dimensionality reduction, comparison datasets are extracted from each cloud virtual machine according to the relationship between the cloud virtual machine and the task. In the process of dimensionality reduction, all cloud virtual machines are replaced by comparison datasets, so as to reduce the number of data comparison and the calculation time.

Improved t-SNE in Anomaly Detection of Cloud Virtual Machine