Accurate Performance Predictions with Component-based Models of Data Streaming Applications
Best paper candidate
Data streaming applications are an important class of data-intensive systems and performance is an essential quality of such systems. Current component-based performance prediction approaches are not sufficient for modeling and predicting the performance of those systems, because the models require elaborate manual engineering to approximate the behavior of data streaming applications that include stateful asynchronous operations, such as windowing operations, and because the simulations for these models do not support the metrics that are specific to data streaming applications. In this paper, we present a modeling language, a simulation and a case-study-based evaluation of the prediction accuracy of an approach for modeling systems that contain stateful asynchronous operations. Our approach directly represents these operations and simulates their behavior. We compare measurements of relevant performance metrics to performance simulation results for a system that processes smart meter readings. To assess the prediction accuracy of our model, we vary both the configuration of the streaming application, such as window sizes, as well as the characteristics of the input data, i.e., the number of smart meters. Our evaluation shows that our model yields prediction results that are competitive with a state-of-the-art baseline model without incurring the additional manual engineering overhead.
Fri 23 SepDisplayed time zone: Belgrade, Bratislava, Budapest, Ljubljana, Prague change
09:00 - 10:30
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Tools & Demos
A: Moamin Abughazala University of L'Aquila, A: Henry Muccini University of L'Aquila, Italy, A: Mohammad SharafMedia Attached
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Each paper is presented as a 5-minute pitch talk at the beginning. The rest of the session is a discussion.
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