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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

On the classification of fog computing applications: A machine learning perspective

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Autor(es):
Guevara, Judy C. [1] ; Torres, Ricardo da S. [2] ; da Fonseca, Nelson L. S. [1]
Número total de Autores: 3
Afiliação do(s) autor(es):
[1] Univ Estadual Campinas, Inst Comp, BR-13083852 Campinas, SP - Brazil
[2] Norwegian Univ Sci & Technol NTNU, Dept ICT & Nat Sci, Alesund - Norway
Número total de Afiliações: 2
Tipo de documento: Artigo Científico
Fonte: JOURNAL OF NETWORK AND COMPUTER APPLICATIONS; v. 159, JUN 1 2020.
Citações Web of Science: 0
Resumo

Currently, Internet applications running on mobile devices generate a massive amount of data that can be transmitted to a Cloud for processing. However, one fundamental limitation of a Cloud is the connectivity with end devices. Fog computing overcomes this limitation and supports the requirements of time-sensitive applications by distributing computation, communication, and storage services along the Cloud to Things (C2T) continuum, empowering potential new applications, such as smart cities, augmented reality (AR), and virtual reality (VR). However, the adoption of Fog-based computational resources and their integration with the Cloud introduces new challenges in resource management, which requires the implementation of new strategies to guarantee compliance with the quality of service (QoS) requirements of applications. In this context, one major question is how to map the QoS requirements of applications on Fog and Cloud resources. One possible approach is to discriminate the applications arriving at the Fog into Classes of Service (CoS). This paper thus introduces a set of CoS for Fog applications which includes, the QoS requirements that best characterize these Fog applications. Moreover, this paper proposes the implementation of a typical machine learning classification methodology to discriminate Fog computing applications as a function of their QoS requirements. Furthermore, the application of this methodology is illustrated in the assessment of classifiers in terms of efficiency, accuracy, and robustness to noise. The adoption of a methodology for machine learning-based classification constitutes a first step towards the definition of QoS provisioning mechanisms in Fog computing. Moreover, classifying Fog computing applications can facilitate the decision-making process for Fog scheduler. (AU)

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