Fine-grained nested virtual machine performance analysis through first level hypervisor tracing

Nowadays, nested VMs are often being used to address compatibility issues, security concerns, software scaling and continuous integration scenarios. With the increased adoption of nested VMs, there is a need for newer techniques to troubleshoot any unexpected behavior. Because of privacy and security issues, ease of deployment and execution overhead, these investigation techniques should preferably limit their data collection in most cases to the physical host level, without internal access to the VMs. This paper introduces the Nested Virtual Machine Detection Algorithm (NDA) - a host hypervisor based analysis method which can investigate the performance of nested VMs. NDA can uncover the CPU overhead entailed by the host hypervisor and guest hypervisors, and compare it to the CPU usage of Nested VMs. We further developed several graphical views, for the TraceCompass trace visualization tool, to display the virtual CPUs of VMs and their corresponding nested VMs, along with their states. These approaches are based on host hypervisor tracing, which brings a lower overhead (around 1%) as compared to other approaches. Based on our analysis and the implemented graphical views, our techniques can quickly detect different problems and their root causes, such as unexpected delays inside nested VMs.

Uncontrolled Keywords

• Nested Virtualization
• KVM
• Performance Analysis
• LTTng
• TraceCompass
• Process State