Energy-aware environments for the development of green applications for cyber–physical systems

Munoz, Daniel-Jesús; Montenegro, José Antonio; Pinto, Mónica; Fuentes, Lidia

doi:10.1016/j.future.2018.09.006

Cited by 21 publications

(11 citation statements)

References 13 publications

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“…Again, the latter is composed of Libraries and Numerical Parameters trees. All the trees have Boolean features, besides Numerical Parameters, which only contains Integer features (e.g., Encryption Key: 64 bytes [27]). HADAS QAM is a relational database that links NVM solutiontree leaves with QAM dynamic identifiers (since parent features are irrelevant when traceability is considered).…”

Section: Validation and Discussionmentioning

confidence: 99%

“…One of the most valuable uses of VMs is the generation of optimal solutions [8] based on Quality Attributes (QAs) or Non-Functional Requirements (NFRs), e.g., maximise performance or minimise energy consumption [27]. This becomes a tough issue when tackling some emergent domains characterised by intensive variability such as Internet of Things (IoT) and/or Edge Computing (EC) systems [32] that present variations at the hardware (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Category Theory Framework for Variability Models with Non-functional Requirements

Munoz

Gurov

Pinto

et al. 2021

Advanced Information Systems Engineering

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Software Product Lines (SPLs) make use of Variability Models (VMs) as an input to automated reasoners, which are mainly used to generate optimal product configurations according to certain Quality Attributes (QAs). However, VMs and more specifically those including numerical features (i.e., NVMs), do not natively support QAs, and consequently, neither do automated reasoners commonly used in variability resolution. However, those satisfiability and optimisation problems have been covered and refined in other relational models such as databases. Category Theory (CT) is an abstract mathematical theory typically used to capture the common aspects of seemingly dissimilar algebraic structures. We propose a unified relational modelling framework subsuming the structured objects of VMs and QAs and their relationships into algebraic categories. This abstraction allows a combination of automated reasoners over different domains to analyse SPLs. The solutions optimisation can now be natively performed by a combination of automated theorem proving, hashing, balanced-trees and chasing algorithms. We validate this approach by means of the edge computing SPL tool HADAS.

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Section: Validation and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Category Theory Framework for Variability Models with Non-functional Requirements

Munoz

Gurov

Pinto

et al. 2021

Advanced Information Systems Engineering

Self Cite

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“…In the application placement literature, various challenges limit the user‐centric metrics such as QoS and QoE. To address these challenges, reviewed papers attempt to utilize their proposed approaches that are mainly categorized in six classes: exact solutions 21‐26 that most of them are considered as a form of mixed integer programming (MIP) problem, framework‐based, 27‐29 heuristic‐based, 30‐33 machine learning‐based, 34‐37 metaheuristic‐based, 38‐40 and model‐based 41‐43 approaches. These approaches will be reviewed in the following.…”

Section: Related Workmentioning

confidence: 99%

“…On the other hand, Benamer et al 25 presented a heuristic‐based application placement strategy on fog nodes minimizing the overall applications' execution time. Dissimilarly, Salaht et al 26 and Munoz et al 27 considered constraint programming to deploy services on fog nodes. Yousefpour et al 28 presented a framework‐based service provisioning strategy for dynamic application deployment on fog nodes utilizing a greedy algorithm.…”

Section: Related Workmentioning

confidence: 99%

Toward an autonomic approach for Internet of Things service placement using gray wolf optimization in the fog computing environment

2021

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Divers and the huge amount of data produced by the Internet of Things (IoT) applications on the one hand, and inherent limitations of local equipment to handle these data, on the other hand, leads to present emerging closer technologies to the end‐users such as fog computing environment. Nevertheless, despite the numerous advantages of such an environment, it still needs state‐of‐the‐art approaches to cope with some inherent limitations. In the literature, resource placement strategies are generally proposed to address such problems, in which the IoT applications are mapped to fog nodes. However, despite its importance, different approaches attempt to enhance the overall system's performance and users' expectations: none of such approaches is satisfactory. In this article, to deploy IoT applications on fog nodes, an autonomic IoT service placement approach based on the gray wolf optimization scheme is proposed, enhancing the system's performance while considering execution costs. Besides, the autonomic concepts help make an appropriate automanagement system that fits better the fog environment's dynamic behavior. Simulation results demonstrate that the proposed approach outperforms the other approaches and converges to the solution in near‐optimal application deployment on fog nodes in respect of the performance of performing services that are 93.7%, the performance of the average waiting time for performed services that are 100%, the remaining services sent to an extra provisioned period that is zero.

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“…Recently, several approaches have been proposed to deal with the energy consumption of highly configurable software systems. In particular, this concern has been addressed relying on dynamic SPL to reconfigure the system depending on context changes and ensure it continues meeting its green requirements [10,13,19]. These approaches also take feature interactions into account, but they rely on an exhaustive detection of such interactions.…”

Section: Related Workmentioning

confidence: 99%

On reducing the energy consumption of software product lines

Guégain

Quinton

Rouvoy

2021

Proceedings of the 25th ACM International Systems and Software Product Line Conference - Volume A

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Along the last decade, several studies considered green software design as a key development concern to improve the energy efficiency of software. Yet, few techniques address this concern for Software Product Lines (SPL). In this paper, we therefore introduce two approaches to measure and reduce the energy consumption of a SPL by analyzing a limited set of products sampled from this SPL. While the first approach relies on the analysis of individual feature consumptions, the second one takes feature interactions into account to better mitigate energy consumption of resulting products.Our experimental results on a real-world SPL indicate that both approaches succeed to produce significant energy improvements on a large number of products, while consumption data was modeled from a small set of sampled products. Furthermore, we show that taking feature interactions into account leads to more products improved with higher energy savings per product. CCS CONCEPTS• Software and its engineering → Software product lines.

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Energy-aware environments for the development of green applications for cyber–physical systems

Cited by 21 publications

References 13 publications

Category Theory Framework for Variability Models with Non-functional Requirements

Category Theory Framework for Variability Models with Non-functional Requirements

Toward an autonomic approach for Internet of Things service placement using gray wolf optimization in the fog computing environment

On reducing the energy consumption of software product lines

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