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ECSA 2020
Mon 14 - Fri 18 September 2020 L'Aquila, Italy

Data streaming applications are an important class of data-intensive systems. Performance is an essential quality of such systems. It is, for example, expressed by the delay of analyses or the utilization of system resources. Architecture-level decisions such as the configuration of sources, sinks and operations, their deployment or the choice of technology impact the performance. Current component-based performance prediction approaches cannot accurately predict the performance of those systems, because they do neither support the metrics that are specific to data streaming applications nor model the behavior of data stream operations. In particular, operations that group multiple data events and thus introduce timing dependencies between different calls to the system are not represented sufficiently. In this paper, we present an approach for modeling networks of data stream operations including their parameters with the goal of predicting the performance of the resulting composed data streaming application. The approach is based on a component-based performance model with queueing semantics for processing resources. Our evaluation shows that our model can more accurately express the behavior of the system, resulting in a more expressive performance model in comparison with a well-encapsulated component-based model without data stream operations.

Thu 17 Sep
Times are displayed in time zone: (GMT+02:00) Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

14:30 - 15:30: Paper Presentations - S8: Performance and Security at ECSA 2020 Teams Channel
Chair(s): Nicole LevyConservatoire National des Arts et Métiers, Barbora BuhnovaMasaryk University

Virtualization support: Roberta Capuano

ecsa-2020-papers14:30 - 14:50
Chadni IslamThe University of Adelaide, Muhammad Ali Babar, Surya NepalCSIRO
ecsa-2020-journal-first14:50 - 15:10
Walt ScacchiUniversity of California, Irvine, Thomas AlspaughUniversity of California, Irvine
ecsa-2020-papers15:10 - 15:30
Dominik WerleKarlsruhe Institute of Technology, Stephan SeifermannKarlsruhe Institute of Technology, Anne KoziolekKarlsruhe Institute of Technology
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