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Fine-grained preemption analysis for latency investigation across virtual machines

Mohamad Gebai, Francis Giraldeau and Michel Dagenais

Article (2014)

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Abstract

This paper studies the preemption between programs running in different virtual machines on the same computer. One of the current monitoring methods consist of updating the average steal time through collaboration with the hypervisor. However, the average is insufficient to diagnose abnormal latencies in time-sensitive applications. Moreover, the added latency is not directly visible from the virtual machine point of view. The main challenge is to recover the cause of preemption of a task running in a virtual machine, whether it is a task on the host computer or in another virtual machine. We propose a new method to study thread preemption crossing virtual machines boundaries using kernel tracing. The host computer and each monitored virtual machine are traced simultaneously. We developed an efficient and portable trace synchronization method, which is required to account for time offset and drift that occur within each virtual machine. We then devised an algorithm to recover the root cause of preemption between threads at every level. The algorithm successfully detected interactions between multiple competing threads in distinct virtual machines on a multi-core machine.

Uncontrolled Keywords

Virtual machine; Tracing; KVM; LTTng; Performance; CPU

Subjects: 2700 Information technology > 2700 Information technology
Department: Department of Computer Engineering and Software Engineering
Funders: Ericsson
PolyPublie URL: https://publications.polymtl.ca/4879/
Journal Title: Journal of Cloud Computing : Advances, Systems and Applications (vol. 3, no. 1)
Publisher: Springer
DOI: 10.1186/s13677-014-0023-3
Official URL: https://doi.org/10.1186/s13677-014-0023-3
Date Deposited: 19 Apr 2022 13:22
Last Modified: 30 Sep 2023 12:36
Cite in APA 7: Gebai, M., Giraldeau, F., & Dagenais, M. (2014). Fine-grained preemption analysis for latency investigation across virtual machines. Journal of Cloud Computing : Advances, Systems and Applications, 3(1), 23 (15 pages). https://doi.org/10.1186/s13677-014-0023-3

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