Generic Controller Adaptive Load Balancing (GCALB) for SDN Networks

Fault tolerance is an important aspect of network resilience. Fault-tolerance mechanisms are required to ensure high availability and high reliability in different environments. The beginning of software-defined networking (SDN) has both presented new challenges and opened a new era to develop new s...

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Main Author: Wael Hosny Fouad Aly
Format: Article
Language:English
Published: Hindawi Limited 2019-01-01
Series:Journal of Computer Networks and Communications
Online Access:http://dx.doi.org/10.1155/2019/6808693
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spelling doaj-f590824900b04dc386ccb90ef903f8242020-11-25T01:43:43ZengHindawi LimitedJournal of Computer Networks and Communications2090-71412090-715X2019-01-01201910.1155/2019/68086936808693Generic Controller Adaptive Load Balancing (GCALB) for SDN NetworksWael Hosny Fouad Aly0College of Engineering and Technology, American University of the Middle East, KuwaitFault tolerance is an important aspect of network resilience. Fault-tolerance mechanisms are required to ensure high availability and high reliability in different environments. The beginning of software-defined networking (SDN) has both presented new challenges and opened a new era to develop new strategies, standards, and architectures to support fault tolerance. In this paper, a study of fault tolerance is performed for two architectures: (1) a single master with multiple slave controllers and (2) multiple slave controllers. The proposed model is called a Generic Controller Adaptive Load Balancing (GCALB) model for SDNs. GCALB adapts the load among slave controllers based on a GCALB algorithm. Mininet simulation tool is utilized for the experimentation phase. Controllers are implemented using floodlights. Experiment results were conducted using GCALB when master controller is taking the responsibility of distributing switches among four and five slave controllers as a case study. Throughput and response time metrics are used to measure performance. GCALB is compared with two reference algorithms: (1) HyperFlow (Kreutz et al., 2012), and (2) Enhanced Controller Fault Tolerant (ECFT) (Aly and Al-anazi, 2018). Results are promising as the performance of GCALB increased by 15% and 12% when compared to HyperFlow and by 13% and 10% when compared to ECFT in terms of throughput and response time.http://dx.doi.org/10.1155/2019/6808693
collection DOAJ
language English
format Article
sources DOAJ
author Wael Hosny Fouad Aly
spellingShingle Wael Hosny Fouad Aly
Generic Controller Adaptive Load Balancing (GCALB) for SDN Networks
Journal of Computer Networks and Communications
author_facet Wael Hosny Fouad Aly
author_sort Wael Hosny Fouad Aly
title Generic Controller Adaptive Load Balancing (GCALB) for SDN Networks
title_short Generic Controller Adaptive Load Balancing (GCALB) for SDN Networks
title_full Generic Controller Adaptive Load Balancing (GCALB) for SDN Networks
title_fullStr Generic Controller Adaptive Load Balancing (GCALB) for SDN Networks
title_full_unstemmed Generic Controller Adaptive Load Balancing (GCALB) for SDN Networks
title_sort generic controller adaptive load balancing (gcalb) for sdn networks
publisher Hindawi Limited
series Journal of Computer Networks and Communications
issn 2090-7141
2090-715X
publishDate 2019-01-01
description Fault tolerance is an important aspect of network resilience. Fault-tolerance mechanisms are required to ensure high availability and high reliability in different environments. The beginning of software-defined networking (SDN) has both presented new challenges and opened a new era to develop new strategies, standards, and architectures to support fault tolerance. In this paper, a study of fault tolerance is performed for two architectures: (1) a single master with multiple slave controllers and (2) multiple slave controllers. The proposed model is called a Generic Controller Adaptive Load Balancing (GCALB) model for SDNs. GCALB adapts the load among slave controllers based on a GCALB algorithm. Mininet simulation tool is utilized for the experimentation phase. Controllers are implemented using floodlights. Experiment results were conducted using GCALB when master controller is taking the responsibility of distributing switches among four and five slave controllers as a case study. Throughput and response time metrics are used to measure performance. GCALB is compared with two reference algorithms: (1) HyperFlow (Kreutz et al., 2012), and (2) Enhanced Controller Fault Tolerant (ECFT) (Aly and Al-anazi, 2018). Results are promising as the performance of GCALB increased by 15% and 12% when compared to HyperFlow and by 13% and 10% when compared to ECFT in terms of throughput and response time.
url http://dx.doi.org/10.1155/2019/6808693
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