Residential Network Traffic and User Behavior Analysis

Internet usage is changing and the demands on the broadband networks are ever increasing. So it is still crucial to understand today's network traffic and the usage patterns of the end users, which will lead to more efficient network design, energy and costs savings, and improvement of the serv...

Full description

Bibliographic Details
Main Author: Zhang, Yichi
Format: Others
Language:English
Published: KTH, Skolan för informations- och kommunikationsteknik (ICT) 2010
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-27001
id ndltd-UPSALLA1-oai-DiVA.org-kth-27001
record_format oai_dc
spelling ndltd-UPSALLA1-oai-DiVA.org-kth-270012013-01-08T13:49:38ZResidential Network Traffic and User Behavior AnalysisengZhang, YichiKTH, Skolan för informations- och kommunikationsteknik (ICT)2010Internet usage is changing and the demands on the broadband networks are ever increasing. So it is still crucial to understand today's network traffic and the usage patterns of the end users, which will lead to more efficient network design, energy and costs savings, and improvement of the service offered to end users. This thesis aims at finding hidden patterns of traffic and user behavior in a residential fiber based access network. To address the problem, a systematic framework of traffic measurement and analysis is developed. It involves PacketLogic traffic data collecting, MySQL database storing, and traffic and user behavior analysis by using Python scripts.   Our approach provides new insights on residential network traffic properties and Internet user habits of households, covering topics of aggregated traffic pattern, household traffic modeling, traffic and user penetration for applications, grouping analysis by cluster and subscriber, and concurrent application analysis. The analysis solutions we provide are based on open source tools without proprietary, giving the most flexibility for codes modification and distribution. Student thesisinfo:eu-repo/semantics/masterThesistexthttp://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-27001application/pdfinfo:eu-repo/semantics/openAccess
collection NDLTD
language English
format Others
sources NDLTD
description Internet usage is changing and the demands on the broadband networks are ever increasing. So it is still crucial to understand today's network traffic and the usage patterns of the end users, which will lead to more efficient network design, energy and costs savings, and improvement of the service offered to end users. This thesis aims at finding hidden patterns of traffic and user behavior in a residential fiber based access network. To address the problem, a systematic framework of traffic measurement and analysis is developed. It involves PacketLogic traffic data collecting, MySQL database storing, and traffic and user behavior analysis by using Python scripts.   Our approach provides new insights on residential network traffic properties and Internet user habits of households, covering topics of aggregated traffic pattern, household traffic modeling, traffic and user penetration for applications, grouping analysis by cluster and subscriber, and concurrent application analysis. The analysis solutions we provide are based on open source tools without proprietary, giving the most flexibility for codes modification and distribution.
author Zhang, Yichi
spellingShingle Zhang, Yichi
Residential Network Traffic and User Behavior Analysis
author_facet Zhang, Yichi
author_sort Zhang, Yichi
title Residential Network Traffic and User Behavior Analysis
title_short Residential Network Traffic and User Behavior Analysis
title_full Residential Network Traffic and User Behavior Analysis
title_fullStr Residential Network Traffic and User Behavior Analysis
title_full_unstemmed Residential Network Traffic and User Behavior Analysis
title_sort residential network traffic and user behavior analysis
publisher KTH, Skolan för informations- och kommunikationsteknik (ICT)
publishDate 2010
url http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-27001
work_keys_str_mv AT zhangyichi residentialnetworktrafficanduserbehavioranalysis
_version_ 1716529836044845056