Next: Data Characteristics
Up: Characterizing Alert and Browse
Previous: Introduction
Related Work
There have been a number of studies on the access dynamics of web servers
servicing clients over a wired network. These studies include analyses of web
access traces from the perspective of proxies [7,20,21],
browsers [6,9], and servers [4,16]. However, to our
knowledge, all previous web workload studies have been conducted for browse
services only and there are no published studies on notification services.
Consequently, we believe, our analysis of notification services is the
first study of its kind.
Even for the browsing services, most studies analyze web servers serving
clients over wired networks. There are very limited studies on web servers
serving clients over wireless channels. The study closest to ours is the one
done by Kunz et al. [12], which analyzes network traces
generated by a mobile browser application. Specifically, their paper analyzes
user behavior (bytes transferred and time spent on the wireless link) based on the notion of
a session that was chosen to be 90 seconds; however, a different session
period could potentially change their results. The main limitation of their
work is the size of the data analyzed: although the traces were collected over
a period of seven months, only 80K entries were logged. It is unclear whether
the inferences drawn from this study can scale up to large commercial
sites. In contrast, we analyzed traces with millions of entries generated over
a period of 12 days at a large commercial site. Furthermore, their study also
has the limitation that it uses client IP addresses for identifying users;
since IP addresses can be reassigned to different users, it is difficult to
perform an accurate user-based analysis. In our study, since every entry in
the logs contains a unique identifier for every access/notification, we are
able to carry out user-behavior analysis more accurately. In addition, our
study is broader as we focus on user behavior, server load, content, and
document popularity analysis.
Tang and Baker analyzed a seven-week trace of a metropolitan-area packet radio
wireless network, and a twelve-week trace of a building-wide local-area
wireless network [18,19]. Both studies focus on how the networks
were used, e.g., when the networks were most active, how active the
network were, and how often users moved, etc. They did not consider the content
or applications for which people used the wireless networks, which is the
focus of our paper.
Recently, Balachandran et al. [5] analyzed the user behavior
and network performance of an IEEE 802.11 based wireless local area network (LAN)
using a workload captured at a three day technical conference event. Their study
focused on characterizing wireless LAN users for the purpose of coming up with
a parameterized model to describe them. Additionally, they carried out
workload analysis to address the network capacity planning problem.
Their study is very different from ours in terms of analysis, methodology and
objectives. While we focus primarily on wireless browse and notification services,
they consider all network traffic for improving the network performance. Furthermore,
the data-set they captured and analyzed is smaller and significantly different
from the web server traces we analyze.
In the sections that follow, whenever appropriate, we refer to related work
done by other researchers and compare it with our findings.
Next: Data Characteristics
Up: Characterizing Alert and Browse
Previous: Introduction
Lili Qiu
2002-04-17