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Hypertracing: Tracing through virtualization layers

Abderrahmane Benbachir and Michel Dagenais

Article (2021)

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Abstract

Cloud computing enables on-demand access to remote computing resources. It provides dynamic scalability and elasticity with a low upfront cost. As the adoption of this computing model is rapidly growing, this increases the system complexity, since virtual machines (VMs) running on multiple virtualization layers become very difficult to monitor without interfering with their performance. In this paper, we present hypertracing, a novel method for tracing VMs by using various paravirtualization techniques, enabling efficient monitoring across virtualization boundaries. Hypertracing is a monitoring infrastructure that facilitates seamless trace sharing among host and guests. Our toolchain can detect latencies and their root causes within VMs, even for boot-up and shutdown sequences, whereas existing tools fail to handle these cases. We propose a new hypervisor optimization, for handling efficient nested paravirtualization, which allows hypertracing to be enabled in any nested environment without triggering VM exit multiplication. This is a significant improvement over current monitoring tools, with their large I/O overhead associated with activating monitoring within each virtualization layer.

Uncontrolled Keywords

virtual machine; para-virtualization; KVM; performance analysis; tracing

Subjects: 2700 Information technology > 2706 Software engineering
2700 Information technology > 2715 Optimization
Department: Department of Computer Engineering and Software Engineering
Funders: CRSNG/NSERC
Grant number: CRDPJ468687-14
PolyPublie URL: https://publications.polymtl.ca/4206/
Journal Title: IEEE Transactions on Cloud Computing (vol. 9, no. 2)
Publisher: IEEE
DOI: 10.1109/tcc.2018.2874641
Official URL: https://doi.org/10.1109/tcc.2018.2874641
Date Deposited: 02 Mar 2020 12:18
Last Modified: 13 May 2023 01:25
Cite in APA 7: Benbachir, A., & Dagenais, M. (2021). Hypertracing: Tracing through virtualization layers. IEEE Transactions on Cloud Computing, 9(2), 654-669. https://doi.org/10.1109/tcc.2018.2874641

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