Flow conflict eliminations through machine learning for software defoned network

Software-Defined Network (SDN) is a modern approach in networking technologies that enables dynamic and programmatically efficient network configuration for improved performance and network monitoring. Similar to the traditional networks, the SDN system is susceptible to conflicts in flows within th...

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Main Author:	Hussien, Mutaz Hamed
Format:	Thesis
Language:	English
Published:	2021
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/101789/1/MutazHamedHussienPSKE2021.pdf.pdf
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spelling	my-utm-ep.1017892023-07-09T02:21:27Z Flow conflict eliminations through machine learning for software defoned network 2021 Hussien, Mutaz Hamed TK Electrical engineering. Electronics Nuclear engineering Software-Defined Network (SDN) is a modern approach in networking technologies that enables dynamic and programmatically efficient network configuration for improved performance and network monitoring. Similar to the traditional networks, the SDN system is susceptible to conflicts in flows within the network. Flow conflict in SDN occurs in response to adjustment of certain features of flows such as priority, match field, and action. While efforts have been made to address these challenges, the current flow of conflict solutions in SDN has several limitations. First, the control layer does not show nor collect the conflict flows that are affected in the OpenFlow switch. Second, the flow entry detection and classification process are relatively time-consuming. Third, there are no studies on detection methods to avoid flow conflicts using artificial intelligence methods such as Machine Learning (ML) as a solution to flow conflict in SDN. This thesis aims to eliminate flows conflict in SDN by using ML algorithms to detect and classify all flow conflicts in the OpenFlow switch. This thesis aims to develop the flow construction model in the SDN controller, detect the conflict flow using ML algorithm, and classify all the conflict types in the flow table using a classification algorithm. In this work, simulation works were conducted in Mininet software using two types of topologies. Decision trees (DT), support vector machine (SVM), hybrid DT- SVM, and extreme fast decision trees (EFDT) ML algorithms were used to detect the conflicts. The main contribution of this thesis is the development of a flow construction model with conflict rules in the OpenFlow table that enhanced the SDN process. By using accurate and effective ML algorithms designed and implemented in the controller layer, flow conflicts are detected and classified to reduce the adverse effects of conflict in the SDN. The performance of the proposed algorithms was evaluated for their efficiency and effectiveness across a variety of evaluation metrics. The EFDT algorithm produced the best results with a performance accuracy above 90% and 95% in detection and classification respectively for all sizes of flows between 1,000 and 100,000. The proposed algorithms for detection and classification show performance improvements over two different algorithms used as benchmarks. 2021 Thesis http://eprints.utm.my/id/eprint/101789/ http://eprints.utm.my/id/eprint/101789/1/MutazHamedHussienPSKE2021.pdf.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:149106 phd doctoral Universiti Teknologi Malaysia Faculty of Engineering - School of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Hussien, Mutaz Hamed Flow conflict eliminations through machine learning for software defoned network
description	Software-Defined Network (SDN) is a modern approach in networking technologies that enables dynamic and programmatically efficient network configuration for improved performance and network monitoring. Similar to the traditional networks, the SDN system is susceptible to conflicts in flows within the network. Flow conflict in SDN occurs in response to adjustment of certain features of flows such as priority, match field, and action. While efforts have been made to address these challenges, the current flow of conflict solutions in SDN has several limitations. First, the control layer does not show nor collect the conflict flows that are affected in the OpenFlow switch. Second, the flow entry detection and classification process are relatively time-consuming. Third, there are no studies on detection methods to avoid flow conflicts using artificial intelligence methods such as Machine Learning (ML) as a solution to flow conflict in SDN. This thesis aims to eliminate flows conflict in SDN by using ML algorithms to detect and classify all flow conflicts in the OpenFlow switch. This thesis aims to develop the flow construction model in the SDN controller, detect the conflict flow using ML algorithm, and classify all the conflict types in the flow table using a classification algorithm. In this work, simulation works were conducted in Mininet software using two types of topologies. Decision trees (DT), support vector machine (SVM), hybrid DT- SVM, and extreme fast decision trees (EFDT) ML algorithms were used to detect the conflicts. The main contribution of this thesis is the development of a flow construction model with conflict rules in the OpenFlow table that enhanced the SDN process. By using accurate and effective ML algorithms designed and implemented in the controller layer, flow conflicts are detected and classified to reduce the adverse effects of conflict in the SDN. The performance of the proposed algorithms was evaluated for their efficiency and effectiveness across a variety of evaluation metrics. The EFDT algorithm produced the best results with a performance accuracy above 90% and 95% in detection and classification respectively for all sizes of flows between 1,000 and 100,000. The proposed algorithms for detection and classification show performance improvements over two different algorithms used as benchmarks.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Hussien, Mutaz Hamed
author_facet	Hussien, Mutaz Hamed
author_sort	Hussien, Mutaz Hamed
title	Flow conflict eliminations through machine learning for software defoned network
title_short	Flow conflict eliminations through machine learning for software defoned network
title_full	Flow conflict eliminations through machine learning for software defoned network
title_fullStr	Flow conflict eliminations through machine learning for software defoned network
title_full_unstemmed	Flow conflict eliminations through machine learning for software defoned network
title_sort	flow conflict eliminations through machine learning for software defoned network
granting_institution	Universiti Teknologi Malaysia
granting_department	Faculty of Engineering - School of Electrical Engineering
publishDate	2021
url	http://eprints.utm.my/id/eprint/101789/1/MutazHamedHussienPSKE2021.pdf.pdf
_version_	1776100771961503744

Flow conflict eliminations through machine learning for software defoned network

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