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WESSBAS: extraction of probabilistic workload specifications for load testing and performance prediction-a

Christian Vögele1, André van Hoorn2, Eike Schulz3

  • 11fortiss GmbH, 80805 Munich, Germany.

Software and Systems Modeling
|May 1, 2018
PubMed
Summary

This study introduces WESSBAS, an automated approach to extract and transform workload specifications from session logs for accurate load testing and performance prediction. WESSBAS ensures precise workload representation, enhancing system performance evaluation.

Keywords:
Load testingPerformance modelsPerformance predictionWorkload specifications

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Area of Science:

  • Computer Science
  • Software Engineering
  • Performance Engineering

Background:

  • Accurate workload specification is crucial for evaluating application system performance via load testing and model-based prediction.
  • Defining realistic workload specifications is a significant challenge in performance evaluation.
  • Existing methods often struggle to accurately represent real-world workloads.

Purpose of the Study:

  • To automate the extraction and transformation of workload specifications for session-based application systems.
  • To improve the accuracy of workload representations for load testing and model-based performance prediction.
  • To bridge the gap between recorded production system behavior and executable performance evaluation models.

Main Methods:

  • Development of the Workload Extraction and Specification for Session-Based Application Systems (WESSBAS) approach.
  • Utilizing a system- and tool-agnostic domain-specific language (DSL) for layered workload modeling.
  • Automatic extraction of DSL instances from production system session logs.
  • Transformation of extracted specifications into executable formats for tools like Apache JMeter and the Palladio Component Model.

Main Results:

  • The WESSBAS approach successfully automates workload specification extraction and transformation.
  • Evaluations using SPECjEnterprise2010 and World Cup 1998 logs demonstrate high accuracy in matching extracted workloads to measured workloads.
  • Key workload characteristics (session length, arrival rates) and performance metrics (response times, CPU utilization) align closely with real-world data.

Conclusions:

  • The WESSBAS approach provides an effective solution for accurately representing real-world workloads in performance evaluation.
  • Automated workload specification enhances the reliability and efficiency of load testing and model-based performance prediction.
  • The method's accuracy validates its utility for session-based application systems.