Performance Prediction of Component-Based Systems

Becker, Steffen; Grunske, Lars; Mirandola, Raffaela; Overhage, Sven

doi:10.1007/11786160_10

Cited by 61 publications

(42 citation statements)

References 30 publications

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“…These performance-influencing factors are reflected in an explicit context model and a parameterized component specification. Recent surveys [23], [24], [25] show that the clear separation of these factors is one of the key benefits of PCM compared to other architecture-level performance models such as the UML SPT and MARTE profiles [4], CSM [5], or KLAPER [7].…”

Section: A Contextmentioning

confidence: 99%

Automated extraction of architecture-level performance models of distributed component-based systems

Brosig

Huber

Kounev

2011

2011 26th IEEE/ACM International Conference on Automated Software Engineering (ASE 2011)

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Abstract-Modern enterprise applications have to satisfy increasingly stringent Quality-of-Service requirements. To ensure that a system meets its performance requirements, the ability to predict its performance under different configurations and workloads is essential. Architecture-level performance models describe performance-relevant aspects of software architectures and execution environments allowing to evaluate different usage profiles as well as system deployment and configuration options. However, building performance models manually requires a lot of time and effort. In this paper, we present a novel automated method for the extraction of architecture-level performance models of distributed component-based systems, based on monitoring data collected at run-time. The method is validated in a case study with the industry-standard SPECjEnterprise2010 Enterprise Java benchmark, a representative software system executed in a realistic environment. The obtained performance predictions match the measurements on the real system within an error margin of mostly 10-20 percent.

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Section: A Contextmentioning

confidence: 99%

Automated extraction of architecture-level performance models of distributed component-based systems

Brosig

Huber

Kounev

2011

2011 26th IEEE/ACM International Conference on Automated Software Engineering (ASE 2011)

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“…Balsamo et al [1] give an overview of about 20 recent approaches based on queueing networks, stochastic Petri nets, and stochastic process algebra. Becker et al [4] survey performance prediction methods specifically targeting component-based systems. Examples are CB-SPE [6], ROBOCOP [7], and CBML [23].…”

Section: Related Workmentioning

confidence: 99%

“…Researchers have developed several methods in this context, which aim at performance (i.e., response times, throughput, resource utilisation) predictions for componentbased designs [4]. However, there are few real-life case studies involving these component-based methods, which still lack industrial maturity.…”

Section: Introductionmentioning

confidence: 99%

“…To investigate the applicability, we conducted a controlled experiment with 19 computer science students, who analysed the performance of two different componentbased designs using the Palladio method [4] as an example for a CBSPE method. We also let the students apply the well-known SPE method [21], which is not specific for component-based systems, on the same designs and compared the results.…”

Section: Introductionmentioning

confidence: 99%

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An Empirical Investigation of the Applicability of a Component-Based Performance Prediction Method

Martens¹,

Becker

Koziolek³

et al. 2008

Computer Performance Engineering

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Abstract. Component-based software performance engineering (CBSPE) methods shall enable software architects to assess the expected response times, throughputs, and resource utilization of their systems already during design. This avoids the violation of performance requirements. Existing approaches for CBSPE either lack tool support or rely on prototypical tools, who have only been applied by their authors. Therefore, industrial applicability of these methods is unknown. On this behalf, we have conducted a controlled experiment involving 19 computer science students, who analysed the performance of two component-based designs using our Palladio performance prediction approach, as an example for a CBSPE method. Our study is the first of its type in this area and shall help to mature CBSPE to industrial applicability. In this paper, we report on results concerning the prediction accuracy achieved by the students and list several lessons learned, which are also relevant for other methods than Palladio.

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“…It has been recognised that this provides opportunities for very early analysis of NFPs based on early design models. These models can often be transformed into analysis models (e.g., in the form of Petri nets or queuing networks) that can be analysed or simulated by standard tooling [1][2][3][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Modular DSLs for Flexible Analysis: An e-Motions Reimplementation of Palladio

Moreno-Delgado

Durán

Zschaler

et al. 2014

Modelling Foundations and Applications

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Abstract. We address some of the limitations for extending and validating MDE-based implementations of NFP analysis tools by presenting a modular, model-based partial reimplementation of one well-known analysis framework, namely the Palladio Architecture Simulator. We specify the key DSLs from Palladio in the e-Motions system, describing the basic simulation semantics as a set of graph transformation rules. Different properties to be analysed are then encoded as separate, parametrised DSLs, independent of the definition of Palladio. These can then be composed with the base Palladio DSL to generate specific simulation environments. Models created in the Palladio IDE can be fed directly into this simulation environment for analysis. We demonstrate two main benefits of our approach: 1) The semantics of the simulation and the nonfunctional properties to be analysed are made explicit in the respective DSL specifications, and 2) because of the compositional definition, we can add definitions of new non-functional properties and their analyses.

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Performance Prediction of Component-Based Systems

Abstract: Performance prediction of component-based systems a survey from an engineering perspective.

Cited by 61 publications

References 30 publications

Automated extraction of architecture-level performance models of distributed component-based systems

Automated extraction of architecture-level performance models of distributed component-based systems

An Empirical Investigation of the Applicability of a Component-Based Performance Prediction Method

Modular DSLs for Flexible Analysis: An e-Motions Reimplementation of Palladio

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