Fog computing conceptual model

Iorga, Michaela; Feldman, Larry; Barton, Robert; Martin, Michael; Goren, Nedim S.; Mahmoudi, Charif

doi:10.6028/nist.sp.500-325

Cited by 281 publications

(209 citation statements)

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“…The National Institute of Standards and Technology has also recently proposed a conceptual architecture for fog computing . The fog configures as a powerful enabling complement to the IoT+edge and to the IoT+cloud scenarios, featuring a new intermediate layer of cooperating devices that can autonomously run services and complete specific business missions, contiguously with the cloud and with cyber‐physical systems at the edge of the network …”

Section: Introductionmentioning

confidence: 99%

“…The National Institute of Standards and Technology has also recently proposed a conceptual architecture for fog computing. 10 The fog configures as a powerful enabling complement to the IoT+edge and to the IoT+cloud scenarios, featuring a new intermediate layer of cooperating devices that can autonomously run services and complete specific business missions, contiguously with the cloud and with cyber-physical systems at the edge of the network. 11 Overall, fog computing should ensure that computation over the collected data happens wherever it is best placed, based on various application (eg, hardware, software, and quality of service (QoS)) or stakeholder (eg, cost and business-related) requirements.…”

Section: Introductionmentioning

confidence: 99%

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How to place your apps in the fog: State of the art and open challenges

et al. 2019

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Summary Fog computing aims at extending the cloud towards the Internet of things so to achieve improved quality of service and to empower latency‐sensitive and bandwidth‐hungry applications. The fog calls for novel models and algorithms to distribute multiservice applications in such a way that data processing occurs wherever it is best placed, based on both functional and nonfunctional requirements. This survey reviews the existing methodologies to solve the application placement problem in the fog, while pursuing three main objectives. First, it offers a comprehensive overview on the currently employed algorithms, on the availability of open‐source prototypes and on the size of test use cases. Second, it classifies the literature based on the application and fog infrastructure characteristics that are captured by available models, with a focus on the considered constraints and the optimized metrics. Finally, it identifies some open challenges in application placement in the fog.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

How to place your apps in the fog: State of the art and open challenges

et al. 2019

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“…Fog node is the basic component of fog architecture. Fog nodes are either physical (eg, servers, routers, gateways, and switches) or virtual (eg, virtual machines and virtualized switches) . These nodes can communicate with end devices and provide their computation resources.…”

Section: Introductionmentioning

confidence: 99%

Quality of service‐aware approaches in fog computing

Kashani

Rahmani

Navimipour

2020

Int J Communication

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Summary In recent years, fog computing, a novel paradigm, has emerged for location and latency‐sensitive applications. It is a powerful complement for cloud computing that enables provisioning services and resources outside the cloud near the end devices. In a fog system, the existence of several nonhomogenous devices, which are potentially mobile, led to quality of service (QoS) worries. QoS‐aware approaches are presented in various parts of the fog system, and several different QoS factors are taken into account. In spite of the importance of QoS in fog computing, no comprehensive study on QoS‐aware approaches exists in fog computing. Hence, this paper reviews the current research used to guarantee QoS in fog computing. This paper investigates the QoS‐ensuring techniques that fall into three categories: service/resource management, communication management, and application management (published between 2013 and October 2018). Regarding the selected approaches, this paper represents merits, demerits, tools, evaluation types, and QoS factors. Finally, on the basis of the reviewed studies, we suggest some open issues and challenges which are worth further studying and researching in QoS‐aware approaches in fog computing.

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“…Subsequently, an alternative called fog computing has been introduced [5], [6]. In contrast to the cloud, which sends data to a remote location for processing, fog computing allocates substantial amounts of computation, storage, and services toward the edge of a network, i.e., on smart end-devices.…”

Section: Introductionmentioning

confidence: 99%

Data Analytics for Fog Computing by Distributed Online Learning with Asynchronous Update

Zhao

et al. 2019

ICC 2019 - 2019 IEEE International Conference on Communications (ICC)

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Fog computing extends the cloud computing paradigm by allocating substantial portions of computations and services towards the edge of a network, and is, therefore, particularly suitable for large-scale, geo-distributed, and data-intensive applications. As the popularity of fog applications increases, there is a demand for the development of smart data analytic tools, which can process massive data streams in an efficient manner. To satisfy such requirements, we propose a system in which data streams generated from distributed sources are digested almost locally, whereas a relatively small amount of distilled information is converged to a center. The center extracts knowledge from the collected information, and shares it across all subordinates to boost their performances. Upon the proposed system, we devise a distributed machine learning algorithm using the online learning approach, which is well known for its high efficiency and innate ability to cope with streaming data. An asynchronous update strategy with rigorous theoretical support is applied to enhance the system robustness. Experimental results demonstrate that the proposed method is comparable with a model trained over a centralized platform in terms of the classification accuracy, whereas the efficiency and scalability of the overall system are improved.

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Fog computing conceptual model

Cited by 281 publications

References 0 publications

How to place your apps in the fog: State of the art and open challenges

How to place your apps in the fog: State of the art and open challenges

Quality of service‐aware approaches in fog computing

Data Analytics for Fog Computing by Distributed Online Learning with Asynchronous Update

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