4.2 Article Proceedings Paper

WESSBAS: extraction of probabilistic workload specifications for load testing and performance prediction-a model-driven approach for session-based application systems

期刊

SOFTWARE AND SYSTEMS MODELING
卷 17, 期 2, 页码 443-477

出版社

SPRINGER HEIDELBERG
DOI: 10.1007/s10270-016-0566-5

关键词

Workload specifications; Load testing; Performance prediction; Performance models

资金

  1. German Research Foundation (DFG) [DFG-SPP 1593, HO 5721/1-1]
  2. Research Group of the Standard Performance Evaluation Corporation (SPEC)

向作者/读者索取更多资源

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.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.2
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据