WBATimeNet: A deep neural network approach for VM Live Migration in the cloud

Abstract

Live Migration (LM) of Virtual Machines (VMs) is an important activity for most cloud platforms, including Azure. LM impacts the availability of VMs, due to which workloads running on them may get affected adversely. Azure cloud infrastructure consists of many nodes and their associated VMs. Many VMs (up to a few million) are candidates to undergo LM at any given time. During the process of LM, it is essential to ensure very low or no adverse impact on the running workload of the customer. Thus, of the millions of VMs, predicting which ones to live migrate based on their low utilization of resources is a critical task. To solve this, we propose a novel deep learning network WBATimeNet, which uses Multivariate Time Series data of Memory, CPU, and Disk to predict which VM should be live migrated. WBATimeNet is a deep neural network-based architecture that uses White-box Adversarial Training to address the high variability and uncertainty of time series data. The experimental results illustrate that WBATimeNet outperforms baseline models by a large margin and helps maintain the increased availability of VMs in Azure during the LM process.