| Title |
WESSBAS: extraction of probabilistic workload specifications for load testing and performance prediction—a model-driven approach for session-based application systems
|
|---|---|
| Published in |
Software and Systems Modeling, October 2016
|
| DOI | 10.1007/s10270-016-0566-5 |
| Pubmed ID | |
| Authors |
Christian Vögele, André van Hoorn, Eike Schulz, Wilhelm Hasselbring, Helmut Krcmar |
| 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. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 2 | 67% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Science communicators (journalists, bloggers, editors) | 1 | 33% |
| Scientists | 1 | 33% |
| Members of the public | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 1 | 2% |
| Canada | 1 | 2% |
| Unknown | 53 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 11 | 20% |
| Student > Bachelor | 11 | 20% |
| Professor | 5 | 9% |
| Researcher | 3 | 5% |
| Student > Ph. D. Student | 3 | 5% |
| Other | 8 | 15% |
| Unknown | 14 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 32 | 58% |
| Engineering | 3 | 5% |
| Economics, Econometrics and Finance | 2 | 4% |
| Mathematics | 1 | 2% |
| Business, Management and Accounting | 1 | 2% |
| Other | 2 | 4% |
| Unknown | 14 | 25% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 November 2016.
All research outputs
#13,547,035
of 23,849,058 outputs
Outputs from Software and Systems Modeling
#206
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Outputs of similar age
#160,622
of 318,525 outputs
Outputs of similar age from Software and Systems Modeling
#1
of 12 outputs
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So far Altmetric has tracked 721 research outputs from this source. They receive a mean Attention Score of 2.2. This one has gotten more attention than average, scoring higher than 67% of its peers.
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We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.