NDLI: Characterizing individual user behaviors in wlans

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Characterizing individual user behaviors in wlans

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Characterizing individual user behaviors in wlans

Content Provider	ACM Digital Library
Author	Hou, Jennifer C. Hamada, Takeo He, Guanghui Chen, Wei-Peng
Abstract	Modeling and analysis of wireless traffic is fundamental to traffic engineering and resource management. The majority of existing work in this arena has focused on modeling/analyzing aggregate traffic, and little has been done in modeling/analyzing wireless traffic at the per-user level. In this paper, we bridge the gap and perform a detailed analysis and modeling on the traffic generated by individual wireless users, leveraging the data traces collected in a period of 4 months (November 2003 - February 2004) on the Dartmouth campus-wide 802.11 WLANs. Our study indicates that several parameters that characterize the wireless traffic generated by individual users are predictable, such as the traffic volume originating from a user and destined for a specific IP address, the set of destination IP addresses (for which connections initiated by a user are destined), and the patterns by which a wireless user connects to applications. We also model the per-connection traffic volume, the duration, and the interarrival time, of connections issued by a user using Weibull or Pareto distributions, and show that the burstiness of aggregate wireless traffic (which has been reported in the literature) is a direct consequence of the burstiness of the burstiness of traffic generated by individual users. These findings can be used to optimize traffic and resource management and provide better QoS (in terms of availability, resiliency, and performance).
Starting Page	132
Ending Page	137
Page Count	6
File Format	PDF
ISBN	9781595938510
DOI	10.1145/1298126.1298150
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2007-10-23
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Traffic measurement and modeling Wireless traces
Content Type	Text
Resource Type	Article

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