The paper proposes a method for predicting the workload of virtual machines in the cloud infrastructure. Reconstruction probabilities of variational autoencoders were used to provide the prediction. Reconstruction probability is a probability criterion that considers the variability in the distribution of variables. In the proposed approach, the values of the reconstruction probabilities of the variational autoencoder show the workload level of the virtual machines. The results of the experiments showed that variational autoencoders gave better results in predicting the workload of virtual machines compared to simple deep neural networks. The generative characteristics of the variational autoencoders determine the workload level by the data reconstruction.
A scalable framework for smart
COVID
surveillance in the workplace using
Deep Neural Networks
and cloud computing
