Estimating Non-functional Properties of Component-based Software Based on Resource Consumption

Meyerhöfer, Marcus; Meyer-Wegener, Klaus

doi:10.1016/j.entcs.2004.02.066

Cited by 8 publications

(3 citation statements)

References 6 publications

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“…In the component model ProCom [23] designed for embedded systems, state is modelled only statically by a set of component parameters. Further, the component architecture of COMQUAD [20] is using Petri nets as a system behaviour model, however, the dependency of the service call on input data is ommited. A lot of other models claim an ability to express state changes but in many cases they refer to the behavior protocol checking [14], state changes monitoring [21] or performance annotations based on measurements [5].…”

Section: State Of the Art Evaluationmentioning

confidence: 99%

State dependence in performance evaluation of component-based software systems

Kapova

Buhnova

Martens

et al. 2010

Proceedings of the First Joint WOSP/SIPEW International Conference on Performance Engineering

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Integrating rising variability of software systems in performance prediction models is crucial to allow widespread industrial use of performance prediction. One of such variabilities is the dependency of system performance on the context and history-dependent internal state of the system (or its components). The questions that rise for current prediction models are (i) how to include the state properties in a prediction model, and (ii) how to balance the expressiveness and complexity of created models.Only a few performance prediction approaches deal with modelling states in component-based systems. Currently, there is neither a consensus in the definition, nor in the method to include the state in prediction models. For these reasons, we have conducted a state-of-the-art survey of existing approaches addressing their expressiveness to model stateful components. Based on the results, we introduce a classification scheme and present the state-defining and state-dependent model parameters. We extend the Palladio Component Model (PCM), a model-based performance prediction approach, with state-modelling capabilities, and study the performance impact of modelled state. A practical influences of the internal state on software performance is evaluated on a realistic case study.

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Section: State Of the Art Evaluationmentioning

confidence: 99%

State dependence in performance evaluation of component-based software systems

Kapova

Buhnova

Martens

et al. 2010

Proceedings of the First Joint WOSP/SIPEW International Conference on Performance Engineering

View full text Add to dashboard Cite

show abstract

“…Further, the COMQUAD component model [24] is using Petri nets as a system behaviour model, the dependence of a service call on input data is however omitted. A number of other prediction models claim an ability to express state changes, but in many cases, they refer to behaviour protocol checking [15], state changes monitoring [26] or performance annotations based on measurements [6].…”

Section: State Of the Art Evaluationmentioning

confidence: 99%

Stateful component-based performance models

2013

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The accuracy of performance-prediction models is crucial for widespread adoption of performance prediction in industry. One of the essential accuracy-influencing aspects of software systems is the dependence of system behaviour on a configuration, context or history related state of the system, typically reflected with a (persistent) system attribute. Even in the domain of component-based software engineering, the presence of state-reflecting attributes (the so-called internal states) is a natural ingredient of the systems, implying the existence of stateful services, stateful components and stateful systems as such. Currently, there is no consensus on the definition or method to include state-related information in component-based prediction models. Besides the task to identify and localise different types of stateful information across component-based software architecture, the issue is to balance the expressiveness and complexity of prediction models via an effective abstraction of state modelling. In this paper, we identify and classify stateful information in component-based software systems, study the performance impact of the individual state categories, and discuss the costs of their modelling in terms of the increased model size. The observations are formulated into a set of heuristics-guiding software engineers in state modelling. Finally, practical effect of state modelling on software performance is evaluated on a real-world case study, the SPECjms2007 Benchmark. The observed deviation of measurements and predictions was significantly decreased by more precise models of stateful dependencies.

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“…Performance prediction on the basis of bytecode benchmarking has been proposed by several researchers [13,23,25], but no working approach has been presented and no libraries or tools are available. Validation has been attempted in [25], but it was restricted to very few Java API methods, and the actual bytecode instructions were neither analysed nor benchmarked.…”

Section: Related Workmentioning

confidence: 99%

Performance Prediction for Black-Box Components Using Reengineered Parametric Behaviour Models

Kuperberg

Krogmann

Reussner

2008

Component-Based Software Engineering

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Abstract. In component-based software engineering, the response time of an entire application is often predicted from the execution durations of individual component services. However, these execution durations are specific for an execution platform (i.e. its resources such as CPU) and for a usage profile. Reusing an existing component on different execution platforms up to now required repeated measurements of the concerned components for each relevant combination of execution platform and usage profile, leading to high effort. This paper presents a novel integrated approach that overcomes these limitations by reconstructing behaviour models with platform-independent resource demands of bytecode components. The reconstructed models are parameterised over input parameter values. Using platform-specific results of bytecode benchmarking, our approach is able to translate the platform-independent resource demands into predictions for execution durations on a certain platform. We validate our approach by predicting the performance of a file sharing application.

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Estimating Non-functional Properties of Component-based Software Based on Resource Consumption

Cited by 8 publications

References 6 publications

State dependence in performance evaluation of component-based software systems

State dependence in performance evaluation of component-based software systems

Stateful component-based performance models

Performance Prediction for Black-Box Components Using Reengineered Parametric Behaviour Models

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