Model-based auto-scaling of distributed data stream processing applications

Russo, Gabriele Russo

doi:10.1145/3429351.3431741

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…[98]. These systems benefit significantly from Big Data processing [76], allowing them to extract new knowledge and decision-making information from the data streams generated by clusters of IoT devices. Fog computing supports this challenge by enabling distributed computing resources for lightweight data processing tasks, including filtering and preprocessing data before sending it to the cloud.…”

Section: Fog and Edge Computing 21 History And Definitionmentioning

confidence: 99%

Fog computing state of the art: concept and classification of platforms to support distributed computing systems

Kirsanova¹,

Радченко²,

Tchernykh³

2021

Preprint

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As the Internet of Things (IoT) becomes a part of our daily life, there is a rapid growth in the connected devices. A well-established approach based on cloud computing technologies cannot provide the necessary quality of service in such an environment, particularly in terms of reducing data latency. Today, fog computing technology is seen as a novel approach for processing large amounts of critical and time-sensitive data. This article reviews cloud computing technology and analyzes the prerequisites for the evolution of this approach and the emergence of the concept of fog computing. As part of an overview of the critical features of fog computing, we analyze the frequent confusion of the concepts of fog and edge computing. We provide an overview of fog computing technologies: virtualization, containerization, orchestration, scalability, parallel computing environments, as well as a systematic analysis of the most popular platforms that support fog computing. As a result of the analysis, we offer two approaches to classification of the fog computing platforms: by the principle of openness/closure of components and a three-level classification based on the provided platform functionality (Deploy-, Platform-and Ecosystem as a Service).

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Section: Fog and Edge Computing 21 History And Definitionmentioning

confidence: 99%

Fog computing state of the art: concept and classification of platforms to support distributed computing systems

Kirsanova¹,

Радченко²,

Tchernykh³

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…[100]. These systems benefit significantly from Big Data processing [76], allowing them to extract new knowledge and decision-making information from the data streams generated by clusters of IoT devices. Fog computing supports this challenge by enabling distributed computing resources for lightweight data processing tasks, including filtering and preprocessing data before sending it to the cloud.…”

Section: History and Definitionmentioning

confidence: 99%

Fog Computing State of the Art: Concept and Classification of Platforms to Support Distributed Computing Systems

Kirsanova

Радченко

Tchernykh

2021

JSFI

View full text Add to dashboard Cite

As the Internet of Things (IoT) becomes a part of our daily life, there is a rapid growth in the connected devices. A well-established approach based on cloud computing technologies cannot provide the necessary quality of service in such an environment, particularly in terms of reducing data latency. Today, fog computing technology is seen as a novel approach for processing large amounts of critical and time-sensitive data. This article reviews cloud computing technology and analyzes the prerequisites for the evolution of this approach and the emergence of the concept of fog computing. As part of an overview of the critical features of fog computing, we analyze the frequent confusion of the concepts of fog and edge computing. We provide an overview of fog computing technologies: virtualization, containerization, orchestration, scalability, parallel computing environments, as well as systematic analysis of the most popular platforms that support fog computing. As a result of the analysis, we offer two approaches to classification of the fog computing platforms: by the principle of openness/closure of components and by the three-level classification based on the provided platform functionality (Deploy-, Platform-and Ecosystem as a Service).

show abstract

Model-based auto-scaling of distributed data stream processing applications

Cited by 2 publications

References 18 publications

Fog computing state of the art: concept and classification of platforms to support distributed computing systems

Fog computing state of the art: concept and classification of platforms to support distributed computing systems

Fog Computing State of the Art: Concept and Classification of Platforms to Support Distributed Computing Systems

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