Modeling event-based communication in component-based software architectures for performance predictions

Rathfelder, Christoph; Klatt, Benjamin; Sachs, Kai; Kounev, Samuel

doi:10.1007/s10270-013-0316-x

Cited by 12 publications

(5 citation statements)

References 32 publications

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“…Although this transaction shows one of the longest response times within our data subset, based on our experiments, no correlation between the actual response time and the prediction accuracy could be identified. In 2004, [24] stated that deviations up to 25 percent are, according to general conditions, within acceptable ranges, results are also comparable to other approaches such as [25], [26].…”

Section: Model Extractionmentioning

confidence: 73%

Collaborative Software Performance Engineering for Enterprise Applications

Müller

Bosse

Wirth³

et al. 2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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Section: Model Extractionmentioning

confidence: 73%

Collaborative Software Performance Engineering for Enterprise Applications

Müller

Bosse

Wirth³

et al. 2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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“…The main novelty in our approach are so called data channels. The concept is based on event channels as introduced by Rathfelder [16] which are, however, mapped to normal call semantics for a system. We extend the idea of event channels by additional state and active processing inside the channel.…”

Section: Modeling Conceptsmentioning

confidence: 99%

Data Stream Operations as First-Class Entities in Component-Based Performance Models

Werle

Seifermann

Koziolek

2020

Software Architecture

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Data streaming applications are an important class of dataintensive systems. Performance is an essential quality of such systems. It is, for example, expressed by the delay of analysis results 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 not support the metrics that are specific to data streaming applications and only approximate the behavior of data stream operations instead of expressing it explicitly. 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 compared to a well-encapsulated component-based model without data stream operations.

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“…La evaluación de la AS implica el análisis de diferentes atributos de calidad (fracción de los requerimientos no funcionales), donde los externos son visibles en tiempo de ejecución y los internos se focalizan en aspectos estáticos. Muchos enfoques proponen la evaluación de atributos de calidad basada en un análisis cualitativo o cuantitativo (Clements, Kazman, and Klein, 2002;Wen-Li Wang, Dai Pan, and Mei-Hwa Chen, 2006;Sharma and Trivedi, 2007;Fukuzawa and Saeki, 2012;Rathfelder et al, 2013;Christensen and Hansen, 2010). Además, existen trabajos focalizados en mejorar la especificación de la AS para su evaluación (Brosch, 2012).…”

Section: Trabajos Relacionadosunclassified

Un Enfoque Inteligente para Soporte a la Toma de Decisiones de Diseño Arquitectónicas en el Contexto de la Evaluación de Arquitecturas de Software

Bogado

Guzmán

Gonnet

et al. 2018

RTyC

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La Ingeniería de Software necesita herramientas novedosas para alcanzar alta calidad en el software, enfrentando el rol cambiante del mismo. La Arquitectura de Software es clave, ya que afecta directamente a la calidad final. La Evaluación de Arquitecturas de Software valida si la arquitectura cumple con los requerimientos de calidad, implicando decisiones de diseño. La toma de decisiones es un proceso complejo conducido por factores humanos, donde la Inteligencia Artificial puede asistir. Entonces, se propone un enfoque basado en Inteligencia Artificial para ayudar a arquitectos en el proceso de toma de decisiones de diseño conducido por atributos de calidad. Esta versión combina modelos de atributos de calidad y un Agente inteligente, utilizando Aprendizaje por Refuerzo para obtener una política de aplicación de patrones arquitectónicos secuencial mediante simulación. Un estudio de caso y una serie de experimentos ilustran la propuesta con patrones comúnmente utilizados en la industria del software.

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Modeling event-based communication in component-based software architectures for performance predictions

Cited by 12 publications

References 32 publications

Collaborative Software Performance Engineering for Enterprise Applications

Collaborative Software Performance Engineering for Enterprise Applications

Data Stream Operations as First-Class Entities in Component-Based Performance Models

Un Enfoque Inteligente para Soporte a la Toma de Decisiones de Diseño Arquitectónicas en el Contexto de la Evaluación de Arquitecturas de Software

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