Parameter Dependent Performance Specifications of Software Components

Koziolek, Heiko; Happe, Jens; Becker, Steffen

doi:10.1007/11921998_15

Cited by 20 publications

(31 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Users of these architecture frame- The Palladio approach [13] consists of a component-based software architecture development process, a metamodel for component-based modelling (Palladio Component Model) [134], and an architecture simulator (Palladio Simulator) that offers the coupling of Palladio models with different formalisms (see Figure 3.1). These formalisms include layered queueing networks [93], queueing Petri nets [113], Java code for simulation [115] and prototyping [109], and OMNeT++ models for simulation [69]. Furthermore, Palladio models can be converted into and from Use Case Maps [153] for intuitive modelling.…”

Section: Architectural Frameworkmentioning

confidence: 99%

Flexible views for view-based model-driven development

Bürger¹

2013

Proceedings of the 18th International Doctoral Symposium on Components and Architecture

View full text Add to dashboard Cite

Modern software development faces growing size and complexity of the systems that are being developed. To cope with this complexity, several languages and modelling formalisms are used during the development of a system in order to describe it from various view points and at multiple levels of abstraction. These languages and modeling formalisms are specific to the domain of the system under development, to the paradigms that are followed, and to the developer tools being used. For example, in a component-based process, the software architecture may be defined using a component metamodel, while class diagrams are used for the object-oriented design of the system; performance and reliability models express the extra-functional properties; program code in a general-purpose programming language defines the execution semantics, and is used for the implementation of the system. Although all these artefacts follow different concepts and formalisms, they express the same system from various view points and can thus be understood as views on the system under development. The entirety of these views can be identified as a complete definition of the system. Although the usage of multiple formalisms offers the advantage that different developers can describe the system from different view points, and in languages and models that are specially designed for this purpose, it introduces the problem of fragmentation of information across heterogeneous artefacts in different formats, concepts, and languages. With increasing complexity of the systems, this makes navigation through the system description difficult for developers and other roles in the development process. Furthermore, since the concepts and formalisms are managed independently, they can share semantic overlaps, so that a piece of information about the system under i Abstract development can be expressed in several ways and contexts. For example, while a component model may be used to express the prescriptive software architecture of a system, the implementation in a general-purpose programming language also determines an implicit architecture of the system, which leads to redundancies in the system descriptions. The independent evolution of these artefacts can lead to inconsistencies, which cannot be identified automatically if the semantic correspondances between the artefacts are not modelled explicitly.View-based software development processes offer two fundamental principles of adressing this problem: While synthetic approaches integrate the information of heterogeneous models to form a complete system description, projective approaches introduce a common formalism in which all view points of the system can be represented, and generate the views from this central model. While the pure synthetic approach suffers from a quadratic increase of interdependencies between the view types, which become very numerous with a growing number of formalisms that have to be supported, the projective approach faces the problem of providing a common formalism that is ab...

show abstract

Section: Architectural Frameworkmentioning

confidence: 99%

Flexible views for view-based model-driven development

Bürger¹

2013

Proceedings of the 18th International Doctoral Symposium on Components and Architecture

View full text Add to dashboard Cite

show abstract

“…Besides validating a particular methodology, the question of prediction precision is tackled from many other angles, including workload characterization [29,16], or generation of either the executable system [31,13,33,6] or the performance model [15,19]. What these approaches have in common is that they still require Self-archived copy.…”

Section: Related Workmentioning

confidence: 99%

“…Perhaps most notable are dependencies due to locking, which are usually captured by performance models, for example as special tasks in LQN [30] or special places in QPN [4]. Less frequently captured are dependencies due to argument passing, so far only captured in performance models solved through simulation [19]. Otherwise, operation durations are usually assumed statistically independent.…”

mentioning

confidence: 99%

Validating Model-Driven Performance Predictions on Random Software Systems

Babka

Tůma

Bulej

2010

Research Into Practice – Reality and Gaps

View full text Add to dashboard Cite

Abstract. Software performance prediction methods are typically validated by taking an appropriate software system, performing both performance predictions and performance measurements for that system, and comparing the results. The validation includes manual actions, which makes it feasible only for a small number of systems. To significantly increase the number of systems on which software performance prediction methods can be validated, and thus improve the validation, we propose an approach where the systems are generated together with their models and the validation runs without manual intervention. The approach is described in detail and initial results demonstrating both its benefits and its issues are presented.Key words: performance modeling, performance validation, MDD MotivationState of the art in model-driven software performance prediction builds on three related factors: the availability of architectural and behavioral software models, the ability to solve performance models, and the ability to transform the former models into the latter. This is illustrated for example by the survey of modeldriven software performance prediction [3], which points out that the typical approach is to use UML diagrams for specifying both the architecture and the behavior of the software system, and to transform these diagrams into performance models based on queueing networks.Both the models and the methods involved in the prediction process necessarily include simplifying assumptions that help abstract away from some of the complexities of the modeled system, e.g., approximating real operation times with probability distributions or assuming independence of operation times. These simplifications are necessary to make the entire prediction process tractable, but the complexity of the modeled system usually makes it impossible to say how the simplifications influence the prediction precision.Self-archived copy. The original publication is available at www.springerlink.com,

show abstract

“…Variables can be characterised for their value, type, or size in bytes in the PCM, as all these properties may influence performance. Additionally, the number of elements and structure of collections can be characterised (see [14]). Variable characterisations are constant or discrete RandomVariables enabling the use of probability distributions, which is useful for characterising larger user groups with different habits.…”

Section: Service Effect Specificationmentioning

confidence: 99%

“…More detail can be found in [14,4]. Independent and identically distributed random variables: Random variables characterising resource demand are assumed to be stochastically independent.…”

Section: Assumptions and Limitationsmentioning

confidence: 99%

Predicting the Performance of Component-Based Software Architectures with Different Usage Profiles

Koziolek

Becker

Happe

2007

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

Abstract. Performance predictions aim at increasing the quality of software architectures during design time. To enable such predictions, specifications of the performance properties of individual components within the architecture are required. However, the response times of a component might depend on its configuration in a specific setting and the data send to or retrieved from it. Many existing prediction approaches for component-based systems neglect these influences. This paper introduces extensions to a performance specification language for components, the Palladio Component Model, to model these influences. The model enables to predict response times of different architectural alternatives. A case study on a component-based architecture for a web portal validates the approach and shows that it is capable of supporting a design decision in this scenario.

show abstract

Parameter Dependent Performance Specifications of Software Components

Cited by 20 publications

References 10 publications

Flexible views for view-based model-driven development

Flexible views for view-based model-driven development

Validating Model-Driven Performance Predictions on Random Software Systems

Predicting the Performance of Component-Based Software Architectures with Different Usage Profiles

Contact Info

Product

Resources

About