A Multivariate Characterization and Detection of Software Performance Antipatterns

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A Multivariate Characterization and Detection of Software Performance Antipatterns

Avritzer, Alberto A. (author): ESulab Solutions, USA

Britto, Ricardo, 1982- (author): Blekinge Tekniska Högskola,Institutionen för programvaruteknik

Trubiani, Catia (author): Gran Sasso Science Institute, ITA

Russo, Barbara (author): Free University of Bozen-Bolzano, ITA

Janes, Andrea (author): Free University of Bozen-Bolzano, ITA

Camilli, Matteo (author): Free University of Bozen-Bolzano, ITA

Van Hoorn, André V. (author): University of Stuttgart, DEU

Heinrich, Robert (author): Karlsruhe Institute of Technology, DEU

Rapp, Martina (author): FZI Forschungszentrum Informatik, DEU

Henß, Jörg (author): FZI Forschungszentrum Informatik, DEU

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(creator_code:org_t)

2021-04-09
2021
English.
In: ICPE 2021 - Proceedings of the ACM/SPEC International Conference on Performance Engineering. - New York, NY, USA : Association for Computing Machinery, Inc. - 9781450381949 ; , s. 61-72

Related links:: https://bth.diva-por... (primary) (Raw object); show more...; http://bth.diva-port...; https://urn.kb.se/re...; https://doi.org/10.1...; show less...

Conference paper (peer-reviewed)

Abstract Subject headings

Context. Software Performance Antipatterns (SPAs) research has focused on algorithms for the characterization, detection, and solution of antipatterns. However, existing algorithms are based on the analysis of runtime behavior to detect trends on several monitored variables (e.g., response time, CPU utilization, and number of threads) using pre-defined thresholds. Objective. In this paper, we introduce a new approach for SPA characterization and detection designed to support continuous integration/delivery/deployment (CI/CDD) pipelines, with the goal of addressing the lack of computationally efficient algorithms. Method. Our approach includes SPA statistical characterization using a multivariate analysis approach of load testing experimental results to identify the services that have the largest impact on system scalability. More specifically, we introduce a layered decomposition approach that implements statistical analysis based on response time to characterize load testing experimental results. A distance function is used to match experimental results to SPAs. Results. We have instantiated the introduced methodology by applying it to a large complex telecom system. We were able to automatically identify the top five services that are scalability choke points. In addition, we were able to automatically identify one SPA. We have validated the engineering aspects of our methodology and the expected benefits by means of a domain experts' survey. Conclusion. We contribute to the state-of-The-Art by introducing a novel approach to support computationally efficient SPA characterization and detection in large complex systems using performance testing results. We have compared the computational efficiency of the proposed approach with state-of-The-Art heuristics. We have found that the approach introduced in this paper grows linearly, which is a significant improvement over existing techniques. © 2021 ACM.

Subject headings

NATURVETENSKAP -- Data- och informationsvetenskap -- Programvaruteknik (hsv//swe)
NATURAL SCIENCES -- Computer and Information Sciences -- Software Engineering (hsv//eng)

Keyword

multivariate analysis
software performance antipattern characterization
software performance antipattern detection
Computational efficiency
Response time (computer systems)
Scalability
Computationally efficient
Continuous integrations
Decomposition approach
Engineering aspects
Large complex systems
Multivariate analysis approaches
Software performance
Statistical characterization
Multivariant analysis

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NATURAL SCIENCES: NATURAL SCIENCES; and Computer and Inf ...; and Software Enginee ...

Articles in the publication: ICPE 2021 - Proc ...

By the university: Blekinge Institute of Technology

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