Approaching the Model-Driven Generation of Feedback to Remove Software Performance Flaws

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

Koziolek²,

Becker

et al. 2010

142

Quantitative prediction of quality properties (i.e. extrafunctional properties such as performance, reliability, and cost) of software architectures during design supports a systematic software engineering approach. Designing architectures that exhibit a good trade-off between multiple quality criteria is hard, because even after a functional design has been created, many remaining degrees of freedom in the software architecture span a large, discontinuous design space. In current practice, software architects try to find solutions manually, which is time-consuming, can be errorprone and can lead to suboptimal designs. We propose an automated approach to search the design space for good solutions. Starting with a given initial architectural model, the approach iteratively modifies and evaluates architectural models. Our approach applies a multi-criteria genetic algorithm to software architectures modelled with the Palladio Component Model. It supports quantitative performance, reliability, and cost prediction and can be extended to other quantitative quality criteria of software architectures. We validate the applicability of our approach by applying it to an architecture model of a component-based business information system and analyse its quality criteria trade-offs by automatically investigating more than 1200 alternative design candidates.

Section: Limitations and Future Workmentioning

confidence: 99%

Automatically improve software architecture models for performance, reliability, and cost using evolutionary algorithms

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

Koziolek²,

Becker

et al. 2010

142

“…Cortellessa et al [13] propose an approach for automated feedback generation for software performance analysis, which aims at systematically evaluating performance prediction results using step-wise refinement and the detection of performance problem patterns. However, at this point, the approach is not automated.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Approach for Multi-attribute QoS Optimisation in Component Based Software Systems

Research Into Practice – Reality and Gaps

Ardagna

Koziolek³

et al. 2010

Abstract. Design decisions for complex, component-based systems impact multiple quality of service (QoS) properties. Often, means to improve one quality property deteriorate another one. In this scenario, selecting a good solution with respect to a single quality attribute can lead to unacceptable results with respect to the other quality attributes. A promising way to deal with this problem is to exploit multi-objective optimization where the objectives represent different quality attributes. The aim of these techniques is to devise a set of solutions, each of which assures a trade-off between the conflicting qualities. To automate this task, this paper proposes a combined use of analytical optimization techniques and evolutionary algorithms to efficiently identify a significant set of design alternatives, from which an architecture that best fits the different quality objectives can be selected. The proposed approach can lead both to a reduction of development costs and to an improvement of the quality of the final system. We demonstrate the use of this approach on a simple case study.

“…We assume that the performance analysis of the initial model has identified a list of violated requirements, and we also assume that a rule engine has parsed the current software system model to build a complete antipatterns list [4]. The questions tackled in this paper are the following: (i) "What are the most guilty antipatterns?"…”

Section: Fig 1 Performance Analysis Interpretationmentioning

confidence: 99%

“…There is no support to rank and solve antipatterns. More recently, in [4] we have presented an approach to automatically detect performance antipatterns based on model-driven techniques.…”

Section: Related Workmentioning

confidence: 99%

A Process to Effectively Identify “Guilty” Performance Antipatterns

Cortellessa

Fundamental Approaches to Software Engineering

Reussner

et al. 2010

Self Cite

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.