Lesegruppe/2018-07-04

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Datum 2018/07/04 11:30:00 – 2018/07/04 12:30:00
Ort Gebäude 50.34, Raum 348
Vortragende(r) Manar Mazkatli
Forschungsgruppe ARE & QSE
Titel WESSBAS: extraction of probabilistic workload specifications for load testing and performance prediction—a model-driven approach for session-based application systems
Autoren Christian Vögele, André van Hoorn, Eike Schulz, Wilhelm Hasselbring, Helmut Krcmar
PDF https://link.springer.com/article/10.1007%2Fs10270-016-0566-5
URL https://doi.org/10.1007/s10270-016-0566-5
BibTeX http://dblp.org/rec/bibtex/journals/sosym/VogeleHSHK18
Abstract The specification of workloads is required in order to evaluate performance characteristics of application systems using load testing and model-based performance prediction.

Defining workload specifications that represent the real workload as accurately as possible is one of the biggest challenges in both areas. To overcome this challenge, this paper presents an approach that aims to automate the extraction and transformation of workload specifications for load testing and model-based performance prediction of session-based application systems. The approach (WESSBAS) comprises three main components. First, a system- and tool-agnostic domain-specific language (DSL) allows the layered modeling of workload specifications of session-based systems. Second, instances of this DSL are automatically extracted from recorded session logs of production systems. Third, these instances are transformed into executable workload specifications of load generation tools and model-based performance evaluation tools.We present transformations to the common load testing tool Apache JMeter and to the Palladio Component Model. Our approach is evaluated using the industry-standard benchmark SPECjEnterprise2010 and the World Cup 1998 access logs.Workload-specific characteristics (e.g., session lengths and arrival rates) and performance characteristics (e.g., response times and CPU utilizations) show that the extracted workloads match the measured workloads with high accuracy.