Deepview: Virtual Disk Failure Diagnosis and Pattern Detection for Azure

In Infrastructure as a Service (IaaS), virtual machines (VMs) use virtual hard disks (VHDs) provided by a remote storage service via the network. Due to separation of VMs and their VHDs, a new type of failure, called VHD failure, which may be caused by various components in the IaaS stack, becomes the dominating factor that reduces VM availability. The current state-of-the-art approaches fall short in localizing VHD failures because they only look at individual components. In this paper, we designed and implemented a system called Deepview for VHD failure localization. Deepview composes a global picture of the system by connecting all the components together, using individual VHD failure events. It then uses a novel algorithm which integrates Lasso regression and hypothesis testing for accurate and timely failure localization. We have deployed Deepview at Microsoft Azure, one of the largest IaaS providers. Deepview reduced the number of unclassified VHD failure events from tens of thousands to several hundreds. It unveiled new patterns including unplanned top-of-rack switch (ToR) reboots and storage gray failures. Deepview reduced the timeto-detection for incidents to under 10 minutes. Deepview further helped us quantify the implications of some key architectural decisions for the first time, including ToR switches as a single-point-of-failure and the computestorage separation.