Cloud Computing Virtual Machine Workload Prediction Method Based on Variational Autoencoder

Cloud Computing Virtual Machine Workload Prediction Method Based on Variational Autoencoder

Fargana J. Abdullayeva
DOI: 10.4018/IJSSSP.2021070103
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Abstract

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.
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2. E-Government And Cloud Computing

The term e-government is used synonymously with the terms e-democracy, digital government, e-government (Helbig et al., 2009). E-government is more effective form of public administration, and provides more effective services to citizens, ensures the improvement of democratic processes (Grönlund, 2003). The following definition of e-government is as follows: E-government is an innovative transformation of technology, the state can use this system to improve citizen-state relations by providing quality services to citizens.

Cloud computing is a new way to provide services over the Internet. Cloud computing ensure that any resource (hardware, software, network resources) is provided as a service.

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