Context-Tailored Workload Model Generation for Continuous Representative Load Testing

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

Context-Tailored Workload Model Generation for Continuous Representative Load Testing

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

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

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2021

Confidentiality

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

Article name in the collection

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

ISBN

978-1-4503-8194-9

ISSN

—

e-ISSN

—

Number of pages

12

Pages from-to

21-32

Publisher name

ACM

Place of publication

USA

Event location

virtuální

Event date

Apr 19, 2021

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000744413800003