Table of Contents
Big Data Cogn. Comput., Volume 3, Issue 3 (September 2019)
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Cover Story (view full-size image) Evaluating the performance of big data applications is required to efficiently size capacities. [...] Read more. Evaluating the performance of big data applications is required to efficiently size capacities. This paper presents an approach to automatically extract system specifications to predict the performance of applications. It consists of three components. First, a system-agnostic domain-specific language (DSL) allows the modeling of performance-relevant factors. Second, DSL instances are automatically extracted from the measurements of Apache Spark systems. Third, these instances are transformed into simulation-based performance evaluation tools. By adapting DSL instances, our approach enables engineers to predict the performance of applications for different scenarios such as changing data input and resources. We evaluated our approach by predicting the performance of two machine learning applications. Simulation results show accurate prediction errors below 15% for response times and resource utilization. View this paper.