GECA: A Global Edge Computing Architecture

Sittón-Candanedo, Inés

doi:10.14201/0aq02878596

Cited by 3 publications

(2 citation statements)

References 47 publications

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“…Recent studies have also proposed novel architectures for collaborative Edge-Cloud learning and data analytics. A review of existing reference architecture designs of Big Data systems such as FAR-Edge [51] and Global Edge Computing Architecture [135] is presented in [106]. Authors propose a novel reference architecture design of a Big Data system with a focus on the utilization of ML in Edge Computing environments.…”

Section: Combining Data Analytics and Learning On The Edge-to-cloud C...mentioning

confidence: 99%

Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review

Rosendo

Costan

Valduriez

et al. 2022

Journal of Parallel and Distributed Computing

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Section: Combining Data Analytics and Learning On The Edge-to-cloud C...mentioning

confidence: 99%

Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review

Rosendo

Costan

Valduriez

et al. 2022

Journal of Parallel and Distributed Computing

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“…The growth and variety of things connected to the Internet have allowed the environment around us to become intelligent [42][43][44][45]; such is the case of cities, transportation systems, energy systems, homes, health systems, industry, education, logistics, and culture [46][47][48][49]. IoT refers to the interconnection through the Internet of a set of equipment, also called "things," and that we use on a daily basis [50][51][52][53][54].…”

Section: Introductionmentioning

confidence: 99%

Smart Environments through the Internet of Things and Its Impact on University Education: A Systematic Review

Chamorro-Atalaya,

Morales-Romero,

Quispe-Andía

et al. 2023

Int. J. Onl. Eng.

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At present, there is diverse scientific evidence of the contributions of smart environments (SE) that have positively impacted various urban problems. However, the concept of SE is very broad, so it is relevant to investigate how these technological trends have been integrated into the university educational environment. Therefore, the objective of this study is to explore and describe the state of the art on the impact of intelligent environments implemented through the Internet of Things (IoT) in university education. Therefore, a systematic review of the literature was developed. The research was developed with a mixed approach and descriptive scope. From this study, it was determined that the purpose of implementing SE in university education is focused on contributing to the teaching and learning process and managing and optimizing the use of resources provided by the educational environment. In addition, smart classrooms are the type of environments that have been implemented to a greater extent and whose results show a positive impact on indicators such as motivation, participation, interaction, satisfaction, and student attitude. With which it is concluded that universities should reflect on the implementation of institutional policies that lead to the progressive implementation of SE, seeking to transcend from being just simple learning classrooms to sustainable environments that contribute to student health and environmental conservation.

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Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review

Rosendo,

Costan,

Valduriez

et al. 2022

Preprint

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The explosion of data volumes generated by an increasing number of applications is strongly impacting the evolution of distributed digital infrastructures for data analytics and machine learning (ML). While data analytics used to be mainly performed on cloud infrastructures, the rapid development of IoT infrastructures and the requirements for low-latency, secure processing has motivated the development of edge analytics. Today, to balance various trade-offs, ML-based analytics tends to increasingly leverage an interconnected ecosystem that allows complex applications to be executed on hybrid infrastructures where IoT Edge devices are interconnected to Cloud/HPC systems in what is called the Computing Continuum, the Digital Continuum, or the Transcontinuum.Enabling learning-based analytics on such complex infrastructures is challenging. The large scale and optimized deployment of learning-based workflows across the Edge-to-Cloud Continuum requires extensive and reproducible experimental analysis of the application execution on representative testbeds. This is necessary to help understand the performance trade-offs that result from combining a variety of learning paradigms and supportive frameworks. A thorough experimental analysis requires the assessment of the impact of multiple factors, such as: model accuracy, training time, network overhead, energy consumption, processing latency, among others.This review aims at providing a comprehensive vision of the main state-of-the-art libraries and frameworks for machine learning and data analytics available today. It describes the main learning paradigms enabling learning-based analytics on the Edge-to-Cloud Continuum. The main simulation, emulation, deployment systems, and testbeds for experimental research on the Edge-to-Cloud Continuum available today are also surveyed. Furthermore, we analyze how the selected systems provide support for experiment reproducibility. We conclude our review with a detailed discussion of relevant open research challenges and of future directions in this domain such as: holistic understanding of performance; performance optimization of applications;efficient deployment of Artificial Intelligence (AI) workflows on highly heterogeneous infrastructures; and reproducible analysis of experiments on the Computing Continuum.

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GECA: A Global Edge Computing Architecture

Cited by 3 publications

References 47 publications

Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review

Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review

Smart Environments through the Internet of Things and Its Impact on University Education: A Systematic Review

Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review

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