Energy-Efficient Resource Allocation in Fog Computing Networks With the Candidate Mechanism

Huang, Xiaoge; Wu, Fan; Chen, Qianbin; Zhang, Jie

doi:10.1109/jiot.2020.2991481

Cited by 32 publications

(14 citation statements)

References 27 publications

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“…Moreover, Abouaomar et al formulated the resource allocation as a Lyapunov problem [70] to a resource representation scheme which allows exposing the resources of each device through Mobile Edge Computing Application Programming Interfaces (MEC APIs) DGS/MEC-0009 in order to optimize resource allocation by the supervising entity in the fog. Therefore, [71] analyzed the energy efficient (EE) resource allocation problem in fog computing networks with the candidate FNs mechanism to ensure the network loading balance under the transmission performance constraints. In the scenario, the associated computation capability allocated to IoT devices from FNs is related to the historical and current energy consumption.…”

Section: Exact Methodsmentioning

confidence: 99%

Resource Allocation in Fog Computing: A Systematic Review

Salem¹,

Algaphari²

2023

JST

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Despite the importance of resource allocation issues, there is no systematic, comprehensive and detailed survey on resource allocation approaches in the fog-computing context. In this article, we provide a Systematic Literature Review (SLR) on the resource allocation approaches in fog environments in the form of a classical taxonomy to recognize the state-of-the-art mechanisms on this important topic and provide open issues. The presented taxonomy is categorized into three main fields: centralized allocation, decentralized allocation, and integrated allocation (published between 2017 and March 2022). According to what is known in fog computing, load balancing and service placement are among the most important basic parameters that ensure service quality. These fields are classified into four methods, approximate, exact, fundamental, and hybrid. In addition, this article investigates resource allocation metrics with all advantages and limitations related to chosen resource allocation mechanisms in networks.

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Section: Exact Methodsmentioning

confidence: 99%

Resource Allocation in Fog Computing: A Systematic Review

Salem¹,

Algaphari²

2023

JST

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“…Fog computing nodes use edge network deployment for efficient communication, especially in an IoT scenario. Huang et al [23] discussed the energyefficient resource allocation issue in the fog computing networks with respect to the fog nodes and implemented an efficient method for load balancing keeping in mind the network resource limitations. Fog computing technology is used for deployment of real time applications in IoT.…”

Section: Literature Surveymentioning

confidence: 99%

Efficient Resource Allocation in Fog Computing Using QTCS Model

Iyapparaja¹,

Alshammari²,

Kumar³

et al. 2022

Computers, Materials &Amp; Continua

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Infrastructure of fog is a complex system due to the large number of heterogeneous resources that need to be shared. The embedded devices deployed with the Internet of Things (IoT) technology have increased since the past few years, and these devices generate huge amount of data. The devices in IoT can be remotely connected and might be placed in different locations which add to the network delay. Real time applications require high bandwidth with reduced latency to ensure Quality of Service (QoS). To achieve this, fog computing plays a vital role in processing the request locally with the nearest available resources by reduced latency. One of the major issues to focus on in a fog service is managing and allocating resources. Queuing theory is one of the most popular mechanisms for task allocation. In this work, an efficient model is designed to improve QoS with the efficacy of resource allocation based on a Queuing Theory based Cuckoo Search (QTCS) model which will optimize the overall resource management process.

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“…And r j,k = log 2 (1 + γ j,k ) is the spectrum efficiency of the F-UE k who associates with the F-AP j by using the Shannon bound. After R j,k from the F-AP j to the F-UE k is obtained, the energy efficiency η j,k can be calculated by [26]:…”

Section: A System Modelmentioning

confidence: 99%

Energy Efficient User Association, Resource Allocation and Caching Deployment in Fog Radio Access Networks

Liu

Zhang

Long

et al. 2022

IEEE Trans. Veh. Technol.

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The heterogeneous fog radio access networks (Fog-RAN), the integration of fog computing, and traditional heterogeneous radio access networks can be implemented through the next-generation wireless communication networks. However, most of the solutions are limited to the spectrum efficiency optimization, and cross-tier interference existing in the fog access points (F-APs) could affect the network performance seriously. In this paper, the user association, resource allocation (including bandwidth and power), and caching deployment are investigated in the heterogeneous Fog-RAN to consider energy efficiency and cross-tier interference mitigation. Specifically, the user association, resource allocation, and caching strategy are formulated as a non-convex optimization problem and then transformed into a convex problem, which can be solved by a proposed algorithm based on the concept of the alternating direction method of multipliers (ADMM). Then an ADMM-based algorithm is proposed to enhance the energy efficiency of the Fog-RAN. Compared with the current solutions, simulation results illustrate the proposed algorithm's convergence and effectiveness.

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Energy-Efficient Resource Allocation in Fog Computing Networks With the Candidate Mechanism

Cited by 32 publications

References 27 publications

Resource Allocation in Fog Computing: A Systematic Review

Resource Allocation in Fog Computing: A Systematic Review

Efficient Resource Allocation in Fog Computing Using QTCS Model

Energy Efficient User Association, Resource Allocation and Caching Deployment in Fog Radio Access Networks

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