A Software Product Line Design Based Approach for Real-time Scheduling of Reconfigurable Embedded Systems

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“…In [23], the authors deal with reconfiguration of real-time embedded systems to cope with hardware/software faults.…”

Section: A Related Workmentioning

confidence: 99%

Supervisory Control for Dynamic Feature Configuration in Product Lines

Reniers

Thuijsman

2020

2020 Forum for Specification and Design Languages (FDL)

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“…Regarding the challenges related to dynamic variability identified from a systematic mapping of the literature [8,18] proposes to continuously use online learning algorithms to refine the performance influence models to runtime context specifications. In the case of real-time reconfigurations, the challenges lie in defining new software products that meet these requirements at runtime without human intervention [17,19]. Variability management of a DSPL is performed based on variation points corresponding to a set of features selected within the domain to be deployed according to compliance with an adaptation rule [17].…”

Section: Introductionmentioning

confidence: 99%

“…In this article, we address the management of dynamic variability by identifying new variation points at runtime, thus enabling the autonomous management of a selfadaptive system, supporting the problem mentioned above by [3,17,19]. The present work aims to directly address variability management in self-adaptive systems by generating a framework, studying runtime system reconfigurations to identify new points of variation, and extending the range of adaptability of the execution domain.…”

Section: Introductionmentioning

confidence: 99%

Variability Management in Self-Adaptive Systems through Deep Learning: A Dynamic Software Product Line Approach

Aguayo,

Sepúlveda,

Mazo

2024

Electronics

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Self-adaptive systems can autonomously adjust their behavior in response to environmental changes. Nowadays, not only can these systems be engineered individually, but they can also be conceived as members of a family based on the approach of dynamic software product lines. Through systematic mapping, we build on the identified gaps in the variability management of self-adaptive systems; we propose a framework that improves the adaptive capability of self-adaptive systems through feature model generation, variation point generation, the selection of a variation point, and runtime variability management using deep learning and the monitor–analysis–plan–execute–knowledge (MAPE-K) control loop. We compute the permutation of domain features and obtain all the possible variation points that a feature model can possess. After identifying variation points, we obtain an adaptation rule for each variation point of the corresponding product line through a two-stage training of an artificial neural network. To evaluate our proposal, we developed a test case in the context of an air quality-based activity recommender system, in which we generated 11 features and 32 possible variations. The results obtained with the proof of concept show that it is possible to manage identifying new variation points at runtime using deep learning. Future research will employ generating and building variation points using artificial intelligence techniques.

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Supervisory Control for Dynamic Feature Configuration in Product Lines

Thuijsman¹,

Reniers

2024

ACM Trans. Embed. Comput. Syst.

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In this paper a framework for engineering supervisory controllers for product lines with dynamic feature configuration is proposed. The variability in valid configurations is described by a feature model. Behavior of system components is achieved using (extended) finite automata and both behavioral and dynamic configuration constraints are expressed by means of requirements as is common in supervisory control theory. Supervisory controller synthesis is applied to compute a behavioral model in which the requirements are adhered to. For the challenges that arise in this setting, multiple solutions are discussed. The solutions are exemplified in the CIF toolset using a model of a coffee machine. A use case of the much larger Body Comfort System product line is performed to showcase feasibility for industrial-sized systems.

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A Software Product Line Design Based Approach for Real-time Scheduling of Reconfigurable Embedded Systems

Cited by 3 publications

References 14 publications

Supervisory Control for Dynamic Feature Configuration in Product Lines

Supervisory Control for Dynamic Feature Configuration in Product Lines

Variability Management in Self-Adaptive Systems through Deep Learning: A Dynamic Software Product Line Approach

Supervisory Control for Dynamic Feature Configuration in Product Lines

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