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Runtime latency detection and analysis

Julien Desfossez, Mathieu Desnoyers and Michel Dagenais

Article (2016)

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Abstract

Detecting latency-related problems in production environments is usually carried out at the application level with custom instrumentation. This is enough to detect high latencies in instrumented applications but does not provide all the information required to understand the source of the latency and is dependent on manually deployed instrumentation. The abnormal latencies usually start in the operating system kernel because of contention on physical resources or locks. Hence, finding the root cause of a latency may require a kernel trace. This trace can easily represent hundreds of thousands of events per second. In this paper, we propose and evaluate a methodology, efficient algorithms, and concurrent data structures to detect and analyze latency problems that occur at the kernel level. We introduce a new kernel-based approach that enables developers and administrators to efficiently track latency problems in production and trigger actions when abnormal conditions are detected. The result of this study is a working scalable latency tracker and an efficient approach to perform stateful tracing in production.

Uncontrolled Keywords

latency; tracing; cloud computing; real-time

Subjects: 2700 Information technology > 2700 Information technology
2700 Information technology > 2705 Software and development
2700 Information technology > 2713 Algorithms
2700 Information technology > 2720 Computer systems software
Department: Department of Computer Engineering and Software Engineering
Funders: CRSNG/NSERC
Grant number: CRDPJ468687-14
PolyPublie URL: https://publications.polymtl.ca/2989/
Journal Title: Software: Practice and Experience (vol. 46, no. 10)
Publisher: Wiley
DOI: 10.1002/spe.2389
Official URL: https://doi.org/10.1002/spe.2389
Date Deposited: 13 Feb 2018 11:18
Last Modified: 16 May 2023 20:35
Cite in APA 7: Desfossez, J., Desnoyers, M., & Dagenais, M. (2016). Runtime latency detection and analysis. Software: Practice and Experience, 46(10), 1397-1409. https://doi.org/10.1002/spe.2389

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