The Palladio component model for model-driven performance prediction

Becker, Steffen; Koziolek, Heiko; Reussner, Ralf

doi:10.1016/j.jss.2008.03.066

Cited by 518 publications

(402 citation statements)

References 16 publications

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“…On the other hand, quantitative approaches focus on the objective assessment of a single alternative. Here, two general modeling approaches exist [25], namely white-box (also referred to as model-based) techniques as well as black-box (also referred to as measurement-based) techniques [46]. The former describe the internal structure and behavior of alternatives on the basis of common models.…”

Section: Prediction Models In Decision Makingmentioning

confidence: 99%

“…As a result, model-based techniques evolved to assess future system performance and traditional prediction literature heavily relies on queuing networks and discrete event simulation [23]. Unfortunately, such concepts and methods related to performance modeling and analysis are largely unknown to most IT practitioners [24] and, thus, are rarely applied in industry [25]. Main reasons include a generally high time effort in construction and analysis of analytical models [26] as well as essential expert knowledge about the model itself, the system to be implemented and its dependencies which may not be available [27].…”

Section: Introductionmentioning

confidence: 99%

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Capacity Management as a Service for Enterprise Standard Software

Müller

Bosse

Turowski

2017

CSIMQ

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Abstract. Capacity management approaches optimize component utilization from a strong technical perspective. In fact, the quality of involved services is considered implicitly by linking it to resource capacity values. This practice hinders to evaluate design alternatives with respect to given service levels that are expressed in user-centric metrics such as the mean response time for a business transaction. We argue that utilized historical workload traces often contain a variety of performance-related information that allows for the integration of performance prediction techniques through machine learning. Since enterprise applications excessively make use of standard software that is shipped by large software vendors to a wide range of customers, standardized prediction models can be trained and provisioned as part of a capacity management service which we propose in this article. Therefore, we integrate knowledge discovery activities into well-known capacity planning steps, which we adapt to the special characteristics of enterprise applications. Using a realworld example, we demonstrate how prediction models that were trained on a large scale of monitoring data enable cost-efficient measurement-based prediction techniques to be used in early design and redesign phases of planned or running applications. Finally, based on the trained model, we demonstrate how to simulate and analyze future workload scenarios. Using a Pareto approach, we were able to identify cost-effective design alternatives for an enterprise application whose capacity is being managed.

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Section: Prediction Models In Decision Makingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Capacity Management as a Service for Enterprise Standard Software

Müller

Bosse

Turowski

2017

CSIMQ

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“…Section 2 describes our approach to the antipattern ranking, in section 3 we illustrate the application of our approach to a case study (i.e. a web reporting system) in the Palladio Component Model (PCM) [2], Section 4 focuses on the open issues of the proposed approach, Section 5 presents the related work, and finally in Section 6 conclusions are provided.…”

Section: Fig 1 Performance Analysis Interpretationmentioning

confidence: 99%

“…First, we describe the example system and the performance analysis with the Palladio approach [2]. Then, we propose the stepwise application of our approach.…”

Section: Experimenting the Approachmentioning

confidence: 99%

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A Process to Effectively Identify “Guilty” Performance Antipatterns

Cortellessa

Martens

Reussner

et al. 2010

Fundamental Approaches to Software Engineering

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Abstract. The problem of interpreting the results of software performance analysis is very critical. Software developers expect feedbacks in terms of architectural design alternatives (e.g., split a software component in two components and re-deploy one of them), whereas the results of performance analysis are either pure numbers (e.g. mean values) or functions (e.g. probability distributions). Support to the interpretation of such results that helps to fill the gap between numbers/functions and software alternatives is still lacking. Performance antipatterns can play a key role in the search of performance problems and in the formulation of their solutions. In this paper we tackle the problem of identifying, among a set of detected performance antipatterns, the ones that are the real causes of problems (i.e. the "guilty" ones). To this goal we introduce a process to elaborate the performance analysis results and to score performance requirements, model entities and performance antipatterns. The cross observation of such scores allows to classify the level of guiltiness of each antipattern. An example modeled in Palladio is provided to demonstrate the validity of our approach by comparing the performance improvements obtained after removal of differently scored antipatterns.

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Planting parallel program simulation on the cloud

Cuomo

Rak

Villano

2013

Concurrency and Computation

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SummaryThe writing of efficient parallel code has always been a tedious and time‐consuming process. However, the performance prediction prototype tools devised in the last decade come of age now, thanks to the availability of the almost unlimited computing power of clouds. This paper presents the practical use of mJADES, a novel environment for running multiple concurrent simulations in the cloud, to predict the performance of parallel code in multiple working conditions at once. After an introduction on mJADES and its operational aspects, the construction of parallel code performance prediction models will be dealt with. The models and the results obtained for a simple but complete and meaningful case study will be presented, discussing the accuracy obtained by simulation and the time required for performing the whole set of simulations necessary to characterize the program behavior. Copyright © 2013 John Wiley & Sons, Ltd.

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The Palladio component model for model-driven performance prediction

Cited by 518 publications

References 16 publications

Capacity Management as a Service for Enterprise Standard Software

Capacity Management as a Service for Enterprise Standard Software

A Process to Effectively Identify “Guilty” Performance Antipatterns

Planting parallel program simulation on the cloud

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