Visible to the public VM processes state detection by hypervisor tracing

TitleVM processes state detection by hypervisor tracing
Publication TypeConference Paper
Year of Publication2018
AuthorsNemati, H., Dagenais, M. R.
Conference Name2018 Annual IEEE International Systems Conference (SysCon)
Keywordscloud, cloud computing, cloud environments, composability, Data collection, host hypervisor, host kernel tracing, Human Behavior, hypervisor tracing, Instruction sets, interactive trace viewer, Metrics, Monitoring, Performance analysis, physical host level, privacy, pubcrawl, Resiliency, security, Trace Compass, virtual machine, Virtual machine monitors, virtual machines, Virtual machining, Virtual Process, virtual Process State Detection algorithm, virtualisation, virtualization, virtualization privacy, VM processes state detection, VM trace analysis algorithm, vPSD, Wait Analysis, wait state
Abstract

The diagnosis of performance issues in cloud environments is a challenging problem, due to the different levels of virtualization, the diversity of applications and their interactions on the same physical host. Moreover, because of privacy, security, ease of deployment and execution overhead, an agent-less method, which limits its data collection to the physical host level, is often the only acceptable solution. In this paper, a precise host-based method, to recover wait state for the processes inside a given Virtual Machine (VM), is proposed. The virtual Process State Detection (vPSD) algorithm computes the state of processes through host kernel tracing. The state of a virtual Process (vProcess) is displayed in an interactive trace viewer (Trace Compass) for further inspection. Our proposed VM trace analysis algorithm has been open-sourced for further enhancements and for the benefit of other developers. Experimental evaluations were conducted using a mix of workload types (CPU, Disk, and Network), with different applications like Hadoop, MySQL, and Apache. vPSD, being based on host hypervisor tracing, brings a lower overhead (around 0.03%) as compared to other approaches.

URLhttps://ieeexplore.ieee.org/document/8369612/
DOI10.1109/SYSCON.2018.8369612
Citation Keynemati_vm_2018