Metrics on feature models to optimize configuration adaptation at run time

Emiliano, Sanchez Luis; Moisan, Sabine; Rigault, Jean-Paul

doi:10.1109/cmsbse.2013.6604435

Cited by 8 publications

(19 citation statements)

References 4 publications

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“…Finally, although an optimal solution is not mandatory, it is desirable to compute "good-enough" solutions. The proposed Configuration Selection Algorithm (Sanchez et al 2013) meets these requirements.…”

Section: Optimizationmentioning

confidence: 92%

“…This optimization algorithm is based on the Best-First Search schema (Pearl 1984), a well studied technique for combinatorial optimization problems such as configuration selection on feature models. This algorithm offers a set of proven characteristics with respect to correctness, completeness, efficiency, and optimality that meet the requirements for its application on runtime systems (Sanchez et al 2013). …”

Section: Approach: Feature Models For Runtime Adaptationmentioning

confidence: 99%

“…We have considered 4 functions, described in Table 1. Although these functions limit the set of metrics supported by the approach, they are appropriate for a wide range of scenarios and have mathematical properties suitable for optimization using feature models (Sanchez et al 2013).…”

Section: Measurementmentioning

confidence: 99%

“…They have interesting properties about efficiency and optimality when they are equipped with admissible heuristics (Pearl 1984), i.e., a function that never overestimates the value of the best solution. Using relaxed models of the problem (Sanchez et al 2013), we have designed admissible heuristics for the 4 aggregate functions presented in Table 1 and a linear combination of them (equation 1) in such a way that BF* can reach an optimal solution within reasonable time and GBFS can improve the optimality of the approximate solution.…”

Section: Optimizationmentioning

confidence: 99%

“…In a previous work (Sanchez et al 2013), we presented a heuristic search algorithm called CSA (Configuration Selection Algorithm) 1 for solving the optimization problem resulting from selecting a valid configuration of a system based on feature models. This algorithm offers different strategies for leveraging execution efficiency and optimality, and allows us to define different objective functions for comparing configurations and optimizing multiple attributes simultaneously, while adhering to resource restrictions and feature model constraints.…”

mentioning

confidence: 99%

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An approach based on feature models and quality criteria for adapting component-based systems

Sanchez

Díaz-Pace

Zunino

et al. 2015

J Softw Eng Res Dev

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Background: Feature modeling has been widely used in domain engineering for the development and configuration of software product lines. A feature model represents the set of possible products or configurations to apply in a given context. Recently, this formalism has been applied to the runtime (re-)configuration of systems with high variability and running in changing contexts. These systems must adapt by updating their component assembly configuration at runtime, while minimizing the impact of such changes on the quality of service. For this reason the selection of a good system configuration is seen as an optimization problem based on quality attribute criteria. Methods: We propose an approach for system adaptation based on the specification, measurement and optimization of quality attribute properties on feature models. Furthermore, we describe its integration into a platform for supporting the self-adaptation of component-based systems. Feature models are annotated with quality attribute properties and metrics, and then an efficient algorithm is used to deal with the optimization problem. Results and conclusions: Two performance properties -frame processing time and reconfiguration time-are estimated with our model against measurements obtained from the running system to show the accuracy of metrics on feature models for estimating quality attribute properties. The results show evidence that these metrics are reasonably accurate for measuring performance properties on a realistic component-based computer vision system. Keywords: Feature models; Runtime adaptation; Quality attributes; Optimization; Component-based software engineering BackgroundFeature models (Kang et al. 1990) are a simple but powerful formalism for representing commonalities, varying aspects, and configuration rules of software products, which have been mostly used in Software Product Lines (SPLs). In recent works, feature models have been applied for specifying and executing dynamically adaptive systems. These systems can be conceptualized as a dynamic software product line (DSPL) (Hallsteinsen et al. 2008) in which variability and configuration rules are bound and checked at runtime. As in traditional SPLs, feature models are a convenient formalism for representing a DSPL and enable automated reasoning about properties of its adaptive configurations.

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Section: Optimizationmentioning

confidence: 92%

Section: Approach: Feature Models For Runtime Adaptationmentioning

confidence: 99%

Section: Measurementmentioning

confidence: 99%

Section: Optimizationmentioning

confidence: 99%

mentioning

confidence: 99%

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An approach based on feature models and quality criteria for adapting component-based systems

Sanchez

Díaz-Pace

Zunino

et al. 2015

J Softw Eng Res Dev

Self Cite

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Designing service-based applications in the presence of non-functional properties: A mapping study

Neto

Vargas-Solar²,

Costa

et al. 2016

Information and Software Technology

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Constraint programming heuristics for configuring optimal products in multi product lines

Ochoa

González-Rojas

Cardozo

et al. 2019

Information Sciences

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a b s t r a c tNowadays, complex application domains require configuring multi-product lines where product features and constraints among them are specified in several variability models. These variability models are enriched with inter-model constraints representing the existing relations among domain concerns, and with non-functional properties modeled as attributes attached to product features. Multiple techniques use constraint programming to automate the cumbersome task of manually configuring a suitable product. Currently, there are some proposals to improve the performance of constraint solvers when configuring single-model product lines, however configuration scenarios with multiple interrelated and attributed models are not yet targeted. This paper proposes and evaluates three search heuristics used to configure optimal products regarding multi-objective criteria. Results are compared against the default search strategy of the Choco constraint solver. We evaluated the performance for configuring optimal products in four state-of-the-art product lines and 130 generated variability models representing multi-product lines that scale up to 6400 features and 960 constraints. As a result, we observe that the proposed heuristics perform better than the default solver strategy when the variability models scale in terms of features. In contrast, the default strategy and one of the proposed heuristics perform better as the number of interdependencies between variability models increases.

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Metrics on feature models to optimize configuration adaptation at run time

Cited by 8 publications

References 4 publications

An approach based on feature models and quality criteria for adapting component-based systems

An approach based on feature models and quality criteria for adapting component-based systems

Designing service-based applications in the presence of non-functional properties: A mapping study

Constraint programming heuristics for configuring optimal products in multi product lines

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