Traffic classification and management based flow statistics netfpga

The internet bandwidth increased significantly over the past years but the problem of network bandwidth management remained a key issue. One of the major problems associated with bandwidth management is network bottleneck, which is the overcapacity of network traffic due to abnormal application band...

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Bibliographic Details
Main Author:	Ali, Haider
Format:	Thesis
Language:	English
Published:	2018
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/79307/1/HaiderAliMFKE2018.pdf
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id	my-utm-ep.79307
record_format	uketd_dc
spelling	my-utm-ep.793072018-10-14T08:42:12Z Traffic classification and management based flow statistics netfpga 2018 Ali, Haider TK Electrical engineering. Electronics Nuclear engineering The internet bandwidth increased significantly over the past years but the problem of network bandwidth management remained a key issue. One of the major problems associated with bandwidth management is network bottleneck, which is the overcapacity of network traffic due to abnormal application bandwidth usage. With the release of new applications every year, especially P2P applications that require high bandwidth, effective network management has become even more important. Congestion can be caused inside a network by numerous flows and high bandwidth applications that may dominate the total bandwidth allocation, affecting normal users. This report presents an approach to detect and manage high bandwidth traffic flows in a congested network, providing fair bandwidth usage to normal users and restricting bandwidth-heavy applications. Flow statistics information is used for classification of network traffic by applying k-means clustering. An inline rate-limiter technique based on queue management is used for controlling high bandwidth flows. The proposed traffic shapping method queues the header packets of flows that are classified as high bandwidth flows. These modules are integrated into the NetFPGA platform, where decision making is carried out with minimal intervention of network administrators by only updating the classifier model when accuracy falls below a threshold line. It ensure zero intrusion of user privacy and at the same time it is able to reduce the high bandwidth rate, providing fair network usage for home users. 2018 Thesis http://eprints.utm.my/id/eprint/79307/ http://eprints.utm.my/id/eprint/79307/1/HaiderAliMFKE2018.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty 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 Ali, Haider Traffic classification and management based flow statistics netfpga
description	The internet bandwidth increased significantly over the past years but the problem of network bandwidth management remained a key issue. One of the major problems associated with bandwidth management is network bottleneck, which is the overcapacity of network traffic due to abnormal application bandwidth usage. With the release of new applications every year, especially P2P applications that require high bandwidth, effective network management has become even more important. Congestion can be caused inside a network by numerous flows and high bandwidth applications that may dominate the total bandwidth allocation, affecting normal users. This report presents an approach to detect and manage high bandwidth traffic flows in a congested network, providing fair bandwidth usage to normal users and restricting bandwidth-heavy applications. Flow statistics information is used for classification of network traffic by applying k-means clustering. An inline rate-limiter technique based on queue management is used for controlling high bandwidth flows. The proposed traffic shapping method queues the header packets of flows that are classified as high bandwidth flows. These modules are integrated into the NetFPGA platform, where decision making is carried out with minimal intervention of network administrators by only updating the classifier model when accuracy falls below a threshold line. It ensure zero intrusion of user privacy and at the same time it is able to reduce the high bandwidth rate, providing fair network usage for home users.
format	Thesis
qualification_level	Master's degree
author	Ali, Haider
author_facet	Ali, Haider
author_sort	Ali, Haider
title	Traffic classification and management based flow statistics netfpga
title_short	Traffic classification and management based flow statistics netfpga
title_full	Traffic classification and management based flow statistics netfpga
title_fullStr	Traffic classification and management based flow statistics netfpga
title_full_unstemmed	Traffic classification and management based flow statistics netfpga
title_sort	traffic classification and management based flow statistics netfpga
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2018
url	http://eprints.utm.my/id/eprint/79307/1/HaiderAliMFKE2018.pdf
_version_	1747818196495237120

Traffic classification and management based flow statistics netfpga

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