Context-Tailored Workload Model Generation for Continuous Representative Load Testing

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10438391" target="_blank" >RIV/00216208:11320/21:10438391 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1145/3427921.3450240" target="_blank" >https://doi.org/10.1145/3427921.3450240</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3427921.3450240" target="_blank" >10.1145/3427921.3450240</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Context-Tailored Workload Model Generation for Continuous Representative Load Testing

Popis výsledku v původním jazyce

Load tests evaluate software quality attributes, such as performance and reliability, by e.g., emulating user behavior that is representative of the production workload. Existing approaches extract workload models from recorded user requests. However, a single workload model cannot reflect the complex and evolving workload of today&apos;s applications, or take into account workload-influencing contexts, such as special offers, incidents, or weather conditions. In this paper, we propose an integrated framework for generating load tests tailored to the context of interest, which a user can describe in a language we provide. The framework applies multivariate time series forecasting for extracting a context-tailored load test from an initial workload model, which is incrementally learned by clustering user sessions recorded in production and enriched with relevant context information. We evaluated our approach with the workload of a student information system. Our results show that incrementally learned workload models can be used for generating tailored load tests. The description language is able to express the relevant contexts, which, in turn, improve the representativeness of the load tests. We have also found that the existing workload characterization concepts and forecasting tools used are limited in regard to strong workload fluctuations, which needs to be tackled in future work.

Název v anglickém jazyce

Context-Tailored Workload Model Generation for Continuous Representative Load Testing

Popis výsledku anglicky

Load tests evaluate software quality attributes, such as performance and reliability, by e.g., emulating user behavior that is representative of the production workload. Existing approaches extract workload models from recorded user requests. However, a single workload model cannot reflect the complex and evolving workload of today&apos;s applications, or take into account workload-influencing contexts, such as special offers, incidents, or weather conditions. In this paper, we propose an integrated framework for generating load tests tailored to the context of interest, which a user can describe in a language we provide. The framework applies multivariate time series forecasting for extracting a context-tailored load test from an initial workload model, which is incrementally learned by clustering user sessions recorded in production and enriched with relevant context information. We evaluated our approach with the workload of a student information system. Our results show that incrementally learned workload models can be used for generating tailored load tests. The description language is able to express the relevant contexts, which, in turn, improve the representativeness of the load tests. We have also found that the existing workload characterization concepts and forecasting tools used are limited in regard to strong workload fluctuations, which needs to be tackled in future work.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

ICPE &apos;21: Proceedings of the ACM/SPEC International Conference on Performance Engineering

ISBN

978-1-4503-8194-9

ISSN

—

e-ISSN

—

Počet stran výsledku

12

Strana od-do

21-32

Název nakladatele

ACM

Místo vydání

USA

Místo konání akce

virtuální

Datum konání akce

19. 4. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000744413800003