Deployment of Fog Computing Devices in Mobile edge Computing environnment
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Date
2022-06-10
Journal Title
Journal ISSN
Volume Title
Publisher
UNIVERSITY of M'SILA
Abstract
Fog Computing has emerged as a favorable technology that can bring Cloud applications
closer to physical IoT devices at the network edge, but there is neither common fog
Computing architecture or how it supports real-time internet. Execution of the service of
objects (IoT). Peripherals such as peripherals such as the switch, router, gateway, mobile
phones, smart car, etc., are the Candidates for deploying fog nodes, but the deployment
differs by application. In this work, we took gateways as candidates for the deployment
of fog nodes. The gateway collects data from smart sensors, but it does not have any preprocessing or decision-making capabilities. Therefore, the Gateway is made smarter with
Fog capabilities and named as Fog Smart Gateway (FSG). IoT traffic processing is
supported by Virtual Machines (VMs) facilitated by distributed Fog nodes. We have
optimized the number of fog nodes to reduce the total latency induced by traffic
aggregation and Processing. Our results show that the optimal deployment of fog IoT
network nodes could result in reduced latency compared to processing IoT data in a
conventional Cloud system.
Moving from theory to practice in fog networks poses the question of the optimal number
of fog nodes that will be upgraded from existing nodes. This paper finds the optimal
number of fog nodes for a total number of ordinary nodes residing in the area of interest
for different channel conditions. Determining the optimal number of fog nodes is highly
beneficial, as it can strongly affect the SINR, and thus the average data rate and
transmission delay. The numerical results indicate that the average data rate increases by
almost an order of magnitude for an optimized number of fog nodes under shading and
fading. It is further shown that the optimal number of fog nodes does not increase in
direct proportion to the increase in the total number of nodes. Moreover, the optimal
number of fog nodes decreases when the channels have high path loss exponents. These
results suggest that fog nodes should be selected among those with the highest
computational capacity for densely deployed networks and channels with high path loss
exponents. Index terms Fog networking, hierarchical networks, SINR, average
throughput, transmission delay.
This work investigates the weighted dynamic deployment of mobile fog-Computing
devices to support a mobile edge Computing environment, in which each peripheral
device is associated with a weight to reflect its importance depending on the application.
Since peripheral devices are mobile and can be switched off, it is difficult to dynamically
optimize the deployment to adapt to the dynamic currency. This work further models the
problem mathematically and solves it with a bat-inspired (BA) algorithm, which finds the
optimal solutions by simulating the foraging behavior of bats via echolocation. Moreover,
three local search methods designed specifically for this problem are integrated into the
BA, and a dynamic local search selection mechanism is proposed to adjust the
probabilities of choosing the three local search methods iteratively in the main loop of
BA. The simulation results show outperformance of the proposed BA compared to the
BA without local search and the previous approach.
Description
Keywords
Fog Computing. Movable edge. Algorithm inspired by nature. Deployment. Dynamic. Graph weighted by nodes, Edge devices, Fog node, Service Latency, virtual machines.