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Towards solving the challenge of minimal overhead monitoring

Towards solving the challenge of minimal overhead monitoring
Towards solving the challenge of minimal overhead monitoring

The examination of performance changes or the performance behavior of a software requires the measurement of the performance. This is done via probes, i.e., pieces of code which obtain and process measurement data, and which are inserted into the examined application. The execution of those probes in a singular method creates overhead, which deteriorates performance measurements of calling methods and slows down the measurement process. Therefore, an important challenge for performance measurement is the reduction of the measurement overhead. To address this challenge, the overhead should be minimized. Based on an analysis of the sources of performance overhead, we derive the following four optimization options: (1) Source instrumentation instead of AspectJ instrumentation, (2) reduction of measurement data, (3) change of the queue and (4) aggregation of measurement data. We evaluate the effect of these optimization options using the MooBench benchmark. Thereby, we show that these optimizations options reduce the monitoring overhead of the monitoring framework Kieker. For MooBench, the execution duration could be reduced from 4.77 μs to 0.39 μs per method invocation on average.

benchmarking, monitoring overhead, performance measurement, software performance engineering
381-388
Association for Computing Machinery
Reichelt, David Georg
5fb209f3-c0f3-452b-92a5-ebde43a49ce0
Kühne, Stefan
1a264da8-4731-430a-bbca-83ec4e404db5
Hasselbring, Wilhelm
ee89c5c9-a900-40b1-82c1-552268cd01bd
Reichelt, David Georg
5fb209f3-c0f3-452b-92a5-ebde43a49ce0
Kühne, Stefan
1a264da8-4731-430a-bbca-83ec4e404db5
Hasselbring, Wilhelm
ee89c5c9-a900-40b1-82c1-552268cd01bd

Reichelt, David Georg, Kühne, Stefan and Hasselbring, Wilhelm (2023) Towards solving the challenge of minimal overhead monitoring. In ICPE '23 Companion: Companion of the 2023 ACM/SPEC International Conference on Performance Engineering. Association for Computing Machinery. pp. 381-388 . (doi:10.1145/3578245.3584851).

Record type: Conference or Workshop Item (Paper)

Abstract

The examination of performance changes or the performance behavior of a software requires the measurement of the performance. This is done via probes, i.e., pieces of code which obtain and process measurement data, and which are inserted into the examined application. The execution of those probes in a singular method creates overhead, which deteriorates performance measurements of calling methods and slows down the measurement process. Therefore, an important challenge for performance measurement is the reduction of the measurement overhead. To address this challenge, the overhead should be minimized. Based on an analysis of the sources of performance overhead, we derive the following four optimization options: (1) Source instrumentation instead of AspectJ instrumentation, (2) reduction of measurement data, (3) change of the queue and (4) aggregation of measurement data. We evaluate the effect of these optimization options using the MooBench benchmark. Thereby, we show that these optimizations options reduce the monitoring overhead of the monitoring framework Kieker. For MooBench, the execution duration could be reduced from 4.77 μs to 0.39 μs per method invocation on average.

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More information

Published date: 15 April 2023
Venue - Dates: 14th Annual ACM/SPEC International Conference on Performance Engineering, ICPE 2023, , Coimbra, Portugal, 2023-04-15 - 2023-04-19
Keywords: benchmarking, monitoring overhead, performance measurement, software performance engineering

Identifiers

Local EPrints ID: 488812
URI: http://eprints.soton.ac.uk/id/eprint/488812
PURE UUID: 701129fe-a024-4faa-a5b7-a3955425a78f
ORCID for Wilhelm Hasselbring: ORCID iD orcid.org/0000-0001-6625-4335

Catalogue record

Date deposited: 05 Apr 2024 16:45
Last modified: 10 Apr 2024 02:15

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Contributors

Author: David Georg Reichelt
Author: Stefan Kühne
Author: Wilhelm Hasselbring ORCID iD

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