Performance Prediction of Virtual Machines via Transfer Learning of Bayesian Network

Bayesian Network (BN) can quantify the uncertain relationships among the multiple Virtual Machine (VM) related features and then predict the VM performance accurately. However, when the settings of hardware/software or the loads running on the VMs change over time, the VM-related features might be different, which will lead to the modification of VM performance prediction results. Thus, we resort to the transfer learning method and then propose a novel BN updating model, called BN_Transfer. BN_Transfer revises the weights of the updated instances constantly, and then combine the Maximum Likelihood Estimation and the hill-climbing methods to modify the parameters and structures of BN accordingly. The experiments conducted on the Alibaba published datasets and the benchmark running results on our simulated platform have shown that the BN_ Transfer can update the BN effectively as well as predict the performance of VM accurately.