Transfer learning of Bayesian network for measuring QoS of virtual machines

Abstract

The Quality of Service (QoS) of virtual machines (VMs) are ensured through the Service Level Agreements (SLAs) signed between the consumers and the cloud providers. A main way to avoid the SLAs violation is to analyze the relationships among the multiple VM-related features and then measure the QoS of VMs accurately. Therefore, we first propose to construct a QoS Bayesian Network (QBN), so as to quantify the uncertain dependencies among the VM-related features and then measure the QoS of VMs effectively. Moreover, we show that the dynamical changes of hardware\software setting or the different types of loads will affect the measurement decisions of QBN. Thus, we further resort to the instance-based transfer learning and then propose a novel QBN updating method (QBNtransfer). QBNtransfer re-weights the constantly updated data instances, and then combine the Maximum Likelihood Estimation and the hill-climbing methods to revise the parameters and structures of QBN accordingly. The experiments conducted on the Alibaba published datasets and the benchmark running results on our simulated platform have shown that the QBN can measure the QoS of VMs accurately and QBNtransfer can update the QBN effectively.