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.

scholarly journals RHLB: Improved Routing Load Balancing Algorithm Based on Hybrid Policy

2019 ◽  
Vol 27 (1) ◽  
pp. 324-337
Author(s):  
Muna Mohammed Jawad ◽  
Noor Mohammed Mahdi
Keyword(s):  
Load Balancing ◽  
Packet Loss ◽  
Network Performance ◽  
Delivery Ratio ◽  
Network Simulator ◽  
Network Load ◽  
Dynamic Policy ◽  
Network Load Balancing ◽  
Load Balancing Algorithm ◽  
Network Overhead

A network is defined as a set of nodes that are associated with a way to handle and transfer data and messages from source to destination. The congestion in the network occurs when a lot of traffic occurs, leads to delay, packet loss, bandwidth degradation, and high network overhead. Load balancing algorithms have been designed to reduce congestion in the network. Load Balancing is the redistribution of workload between two or more nodes to be executed at the same time. Two policies of load balancing algorithms: static and dynamic load balancing. This paper proposes a load balancing algorithm based on the hybrid (static and dynamic) policy using Network Simulator (version 2). The hybrid policy is used to improve network performance by redistributing the load between overloaded nodes to other nodes that are under loaded when congestion occurs. The simulation results show that the proposed algorithm used performance of the network with regard to throughput, packet delivery ratio, packet loss and the end-to-end delay.

M2FBalancer: A mist-assisted fog computing-based load balancing strategy for smart cities

2021 ◽  
pp. 1-15
Author(s):  
Subhranshu Sekhar Tripathy ◽  
Diptendu Sinha Roy ◽  
Rabindra K. Barik
Keyword(s):  
Load Balancing ◽  
Resource Utilization ◽  
Smart City ◽  
Load Transfer ◽  
Smart Cities ◽  
Fog Computing ◽  
Round Robin ◽  
Healthcare Management ◽  
Optimization Strategy ◽  
Allocation Method

Nowadays, cities are intended to change to a smart city. According to recent studies, the use of data from contributors and physical objects in many cities play a key element in the transformation towards a smart city. The ‘smart city’ standard is characterized by omnipresent computing resources for the observing and critical control of such city’s framework, healthcare management, environment, transportation, and utilities. Mist computing is considered a computing prototype that performs IoT applications at the edge of the network. To maintain the Quality of Service (QoS), it is impressive to employ context-aware computing as well as fog computing simultaneously. In this article, the author implements an optimization strategy applying a dynamic resource allocation method based upon genetic algorithm and reinforcement learning in combination with a load balancing procedure. The proposed model comprises four layers i.e. IoT layer, Mist layer, Fog layer, and Cloud layer. Authors have proposed a load balancing technique called M2F balancer which regulates the traffic in the network incessantly, accumulates the information about each server load, transfer the incoming query, and disseminate them among accessible servers equally using dynamic resources allocation method. To validate the efficacy of the proposed algorithm makespan, resource utilization, and the degree of imbalance (DOI) are considered as the scheduling parameter. The proposed method is being compared with the Least count, Round Robin, and Weighted Round Robin. In the end, the results demonstrate that the solutions enhance QoS in the mist assisted cloud environment concerning maximization resource utilization and minimizing the makespan. Therefore, M2FBalancer is an effective method to utilize the resources efficiently by ensuring uninterrupted service. Consequently, it improves performance even at peak times.

scholarly journals Evaluating Modern Data Centre Transport Protocols in OMNeT++/INET

10.29007/h27x ◽  
2019 ◽  
Author(s):  
Mohammed Alasmar ◽  
George Parisis
Keyword(s):  
Load Balancing ◽  
Simulation Model ◽  
Network Topology ◽  
Transport Protocol ◽  
Data Transport ◽  
Transport Protocols ◽  
Data Centre ◽  
Evaluation Platform ◽  
Data Centres

In this paper we present our work towards an evaluation platform for data centre transport protocols. We developed a simulation model for NDP1, a modern data transport protocol in data centres, a FatTree network topology and per-packet ECMP load balancing. We also developed a data centre environment that can be used to evaluate and compare data transport protocols, usch as NDP and TCP. We describe how we integrated our model with the INET Framework and present example simulations to showcase the workings of the developed framework. For that, we ran a comprehensive set of experiments and studied different components and parameters of the developed models.

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