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virtFlow: guest independent execution flow analysis across virtualized environments

Hani Nemati and Michel R. Dagenais

Article (2018)

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Cite this document: Nemati, H. & Dagenais, M. R. (2018). virtFlow: guest independent execution flow analysis across virtualized environments. IEEE Transactions on Cloud Computing, p. 1-15. doi:10.1109/tcc.2018.2828846
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Abstract

An agent-less technique to understand virtual machines (VMs) behavior and their changes during the VM life-cycle is essential for many performance analysis and debugging tasks in the cloud environment. Because of privacy and security issues, ease of deployment and execution overhead, the method preferably limits its data collection to the physical host level, without internal access to the VMs. We propose a host-based, precise method to recover execution flow of virtualized environments, regardless of the level of virtualization. Given a VM, the Any-Level VM Detection Algorithm (ADA) and Nested VM State Detection (NSD) Algorithm compute its execution path along with the state of virtual CPUs (vCPUs) from the host kernel trace. The state of vCPUs is displayed in an interactive trace viewer (TraceCompass) for further inspection. Then, a new approach for profiling threads and processes inside the VMs is proposed. Our proposed VM trace analysis algorithms have been open-sourced for further enhancements and to the benefit of other developers. Our new techniques are being evaluated with workloads generated by different benchmarking tools. These approaches are based on host hypervisor tracing, which brings a lower overhead (around 1%) as compared to other approaches.

Uncontrolled Keywords

execution flow analysis; nested virtual machine; performance analysis, tracing; reverse engineering

Open Access document in PolyPublie
Subjects: 2700 Technologie de l'information > 2706 Génie logiciel
2700 Technologie de l'information > 2715 Optimisation
Department: Département de génie informatique et génie logiciel
Research Center: Non applicable
Funders: CRSNG/NSERC
Grant number: CRDPJ468687-14
Date Deposited: 02 Mar 2020 12:35
Last Modified: 03 Mar 2020 01:20
PolyPublie URL: https://publications.polymtl.ca/4210/
Document issued by the official publisher
Journal Title: IEEE Transactions on Cloud Computing
Publisher: IEEE
Official URL: https://doi.org/10.1109/tcc.2018.2828846

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