Analyzing Browse Patterns of Mobile Clients

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Analyzing Browse Patterns of Mobile Clients

• Lili Qiu
• Joint work with Atul Adya and Victor Bahl
• adya,bahl,liliq_at_microsoft.com
• Microsoft Research
• ACM SIGCOMM Measurement Workshop
• San Francisco, CA, November 2001

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Outline

• Overview
• Related work
• Analysis of a popular mobile Web site
• Document popularity analysis
• User behavior analysis
• System load analysis
• Content analysis
• Summary and implications

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Motivation

• Phenomenal growth in cellular industry and
  handheld device
• Crucial to understand the performance of wireless
  Web
• Limited understanding of how wireless Web
  services are being used

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Related Work

• Workload of clients at wireline networks
• Server-based studies
• ABC96, AW96, MS97, AJ99,PQ00
• Proxy-based studies
• BCF99, DMF97, GB97, VDA99, WVS99
• Client-based studies
• CBC95 and BBB98
• Workload of wireless clients
• KBZ2000
• Only 80K requests over seven months

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Overview

• A popular mobile Web site
• Content
• news, weather, stock quotes, email, yellow pages,
  travel reservations, entertainment etc.
• Period studied
• August 15, 2000 August 26, 2000
• 33 million accesses in 12 days
• Type of analyses
• This paper is a part of larger analysis study
• Analysis of browse pattern
• Analysis of notification logs
• Correlation between how browsing and notification
  services are being used

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Overview Types of Analysis

• Document popularity analysis
• User behavior analysis
• System load analysis
• Content analysis

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Overview User Categories

• Cellular users
• Browse the Web in real time on cellular phones
• Offline users
• Download content onto their PDAs for later
  (offline) browsing, e.g. AvantGo
• Desktop users
• Signup services and specify preferences
• Many more users now

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Document Popularity

• Previous Web research have found Web accesses
  follow Zipf-like distribution (i.e. request
  frequency ? 1/i?)
• Two definitions of document
• URL
• ltURL, parametergt (i.e. query)

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Document Popularity (Cont.)
Document Popularity does not closely follow
Zipf-like distribution.
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Document Popularity (Cont.)

• Majority of the requests are concentrated on a
  small number of documents
• 0.1 - 0.5 URL and parameter combinations (i.e.
  112 442) account for 90 requests

Very small amount of memory needed to cache
popular query results.
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User Behavior Analysis

• Understand how long a wireless user stays on the
  channel as he/she browses the Web
• Determine user sessions
• Intuition a session is idle for a sufficiently
  long time, we say it has ended.
• Heuristic to determine a session inactivity
  period

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User Behavior Analysis (Cont.)

• Determine the session inactivity period (s)
• Too small s gt too many sessions
• Too large s gt too few sessions
• An appropriate value is at the knee point
• The knee point is between 30 to 45 seconds
• 95 users
• Have session time less than 3 minutes
• Initiated less than 35 sessions during the 12
  days

We can reclaim IP addresses more quickly than 90
seconds used previously in KBZ2000.
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System Load Analysis

• Understand how to optimize Web server for better
  performance
• Small replies
• 98 to wireless users lt 3 KB
• 99 to offline users lt 6.3 KB
• Diurnal pattern and weekday vs. weekend variation
• Over 60 browsing requests are from offline PDA
  users, and less than 7 are from wireless users.

1) Highly optimize sending small replies. 2)
Identify what type of user issued the request,
and prioritize the request according to the
user type.
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Content Analysis
Important to content providers what content is
interesting to users
Top three preferences for different kinds of users
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Summary of Results and Implications
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Summary of Results and Implications