Fuzzy Logic Load Balancing for Cloud Architecture Network - A Simulation Test

2017 ◽  
pp. 43-54
Author(s):  
Łukasz Apiecionek ◽  
Jacek M. Czerniak ◽  
Wojciech Dobrosielski ◽  
Dawid Ewald
Keyword(s):  
Fuzzy Logic ◽  
Load Balancing ◽  
Simulation Test ◽  
Cloud Architecture

Virtualized Fog Network with Load Balancing for IoT based Fog-to-Cloud

2020 ◽  
Vol 4 (3) ◽  
Author(s):  
Istabraq M. Al-Joboury ◽  
Emad H. Al-Hemiary
Keyword(s):  
Load Balancing ◽  
Packet Loss ◽  
Fog Computing ◽  
Transport Protocol ◽  
Network Simulator ◽  
Huge Number ◽  
Message Queue ◽  
Limited Bandwidth ◽  
Cloud Architecture ◽  
Fog Network

Fog Computing is a new concept made by Cisco to provide same functionalities of Cloud Computing but near to Things to enhance performance such as reduce delay and response time. Packet loss may occur on single Fog server over a huge number of messages from Things because of several factors like limited bandwidth and capacity of queues in server. In this paper, Internet of Things based Fog-to-Cloud architecture is proposed to solve the problem of packet loss on Fog server using Load Balancing and virtualization. The architecture consists of 5 layers, namely: Things, gateway, Fog, Cloud, and application. Fog layer is virtualized to specified number of Fog servers using Graphical Network Simulator-3 and VirtualBox on local physical server. Server Load Balancing router is configured to distribute the huge traffic in Weighted Round Robin technique using Message Queue Telemetry Transport protocol. Then, maximum message from Fog layer are selected and sent to Cloud layer and the rest of messages are deleted within 1 hour using our proposed Data-in-Motion technique for storage, processing, and monitoring of messages. Thus, improving the performance of the Fog layer for storage and processing of messages, as well as reducing the packet loss to half and increasing throughput to 4 times than using single Fog server.

Optimization of a Fuzzy Logic Controller for Handover-Based Load Balancing

2011 ◽  
Author(s):  
P. Munoz ◽  
R. Barco ◽  
I. de la Bandera ◽  
M. Toril ◽  
S. Luna-Ramirez
Keyword(s):  
Fuzzy Logic ◽  
Load Balancing ◽  
Fuzzy Logic Controller

Performance Profiling of Load Balancing Algorithms in a Cloud Architecture

2021 ◽  
Author(s):  
Samirah Salifu ◽  
Nathan Turlington ◽  
Michael Galloway
Keyword(s):  
Load Balancing ◽  
Performance Profiling ◽  
Cloud Architecture

scholarly journals Optimized Resource Allocation in IoT using Fuzzy Logic and Bio-Inspired Algorithms

2021 ◽  
Author(s):  
Deepak Kumar Sharma ◽  
Jahanavi Mishra ◽  
Aeshit Singh ◽  
Raghav Govil ◽  
Krishna Kant Singh ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Fuzzy Logic ◽  
Load Balancing ◽  
Network Performance ◽  
Global Network ◽  
Smart Devices ◽  
Delivery Ratio ◽  
Iot Devices ◽  
Nature Inspired Algorithms ◽  
Brute Force Approach

Abstract IoT smart devices are a confluence of microprocessors, sensors, power source and transceiver modules to effectively sense, communicate and transfer data. Energy efficiency is a key governing value of the network performance of smart devices in distributed IoT networks.Low and discrete power and limited amount of memory and finite amount of resources form some major bottlenecks in the workflow.Dynamic load balancing, reliability and flexibility are heavily relied upon by cloud computing for its accessibility.Resources are dynamically provided to the end client in an as-come on-demand fashion with the global network that is the Internet. Proportionally the need for services is increasing at a rate that is astonishing compared to any other forms of development. Load balancing seems a major challenge faced due to the architecture and the modular nature of our cloud environment. Loads need to be distributed dynamically to all the nodes. In this paper, we have introduced a technique that combines fuzzy logic with various nature inspired algorithms - grey wolf algorithm and firefly algorithm in order to effectively balance the load in a network of IoT devices. The performances of various nature inspired algorithms are compared with a brute force approach on the basis of energy efficiency, network lifetime maximization, node failure rate and packet delivery ratio.

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