Virtual Machine Performance Prediction Based on Transfer Learning of Bayesian Network

Abstract

The performance of virtual machines in the cloud is fraught with uncertainty due to the complexity of the environment, which poses a challenge for accurate prediction of virtual machine performance. In addition, a well-performing virtual machine performance prediction model cannot be multiplexed in either the temporal or spatial dimensions. In this paper, we build a virtual machine performance prediction model based on Bayesian network to solve the problem of accurate prediction of virtual machine performance. Furthermore, to achieve multiplexing of the performance prediction model in both temporal and spatial dimensions, we propose a Bayesian network transfer learning approach. Experiments show that in our transfer learning approach, in contrast with reconstruction, the amount of data in the training set was reduced by 90%, and the training time was reduced by 75%, while the macro average precision maintaining 79%.