Characterizing Alert and Browse Services for Mobile Clients

1
Characterizing Alert and Browse Services for
Mobile Clients

• Atul Adya, Victor Bahl, Lili Qiu
• Microsoft Research
• USENIX Annual Technical Conference
• Monterey, CA, June 2002

2
Outline

• Motivation
• Related Work
• Overview of Data Logs and Key Results
• Detailed Analysis
• Notification Services
• Browse Services
• Correlation between the Two Services
• Summary and Implications

3
Motivation

• Wireless web services
• Becoming popular
• Crucial to understand usage pattern
• Few existing studies on how they are used

4
Related Work

• Workload of clients at wireline networks
• Server-based studies
• NASA, ClarkNet, MSNBC, WorldCup,
• Proxy-based studies
• NLANR, Digital, UW,
• Client-based studies
• Boston Univ., WebTV,
• Workload of wireless clients
• Kunz et. al. 2000
• Only 80K requests over seven months
• No existing study on notification usage

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Overview

• A popular commercial Web site for mobile clients
• Content
• news, weather, stock quotes, email, yellow pages,
  travel reservations, entertainment etc.
• Services
• Notification
• Browse
• Period studied
• 3.25 million notifications in Aug. 20 26, 2000
• 33 million browse requests in Aug. 15 26, 2000

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Overview User Categories

• Cellular users
• Browse the Web in real time using cellular
  technologies
• Offline users
• Download content onto their PDAs for later
  (offline) browsing, e.g. AvantGo
• Desktop users
• Signup services and specify preferences
• Notification log has 200,860 users (99 were
  wireless users)
• Browse log

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Major Findings

• Notification Services
• Popularity of notification messages follows
  Zipf-like distribution
• Top 1 notification objects account for 54-64 of
  total messages
• Exhibits geographical locality
• Browse Services
• 0.1 - 0.5 urls account for 90 requests
• The set of popular urls remain stable
• Correlation between the two services
• Correlation is limited

8
Outline

• Motivation
• Related Work
• Overview of Data Logs and Key Results
• Detailed Analysis
• Notification Services
• Browse Services
• Correlation between the Two Services
• Summary and Implications

9
Notification Log Analysis

• Types of Analyses
• Content analysis
• Notification message popularity
• User behavior analysis
• Geographical locality

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Content Analysis
Important to content providers and notification
service designers
Popular categories weather, news, stock quotes,
email.
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Notification Message Popularity

• Researchers have found Web accesses follow
  Zipf-like distribution (i.e., request ? 1/i?)

Notification message popularity follows Zipf-like
distribution (? ? 1.1, 1.3) ? generate
synthetic traces
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Notification Msg Popularity (Cont.)

• Notification msgs are highly concentrated on a
  small number of documents
• Top 1 notification documents account for 54 -
  64 of the total messages

Application-level multicast would be an efficient
way of delivering popular notifications.
13
Geographical Locality

• Local sharing
• ? 2 users in the same cluster receive the msg

Notification exhibits geographical locality.
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Outline

• Motivation
• Related Work
• Overview of Data Logs and Key Results
• Detailed Analysis
• Notification Services
• Browse Services
• Correlation between the Two Services
• Summary and Implications

15
Browser Log Analysis

• Types of Analyses
• Content analysis
• Documents popularity
• User behavior analysis
• Temporal stability
• Geographical locality
• Load distribution of different users

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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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Document Popularity

• Two definitions of document
• Base URLs
• Full URLs including parameters

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

• Requests are highly concentrated on a small
  number of documents
• 0.1 - 0.5 full urls (i.e., 112 442) account
  for 90 requests

Very small amount of memory needed to cache
popular query results if content doesnt change.
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Temporal Stability

• Methodology
• Consider 2 days traces
• Pick the top n documents from each day
• Compute overlap

Popular urls remain stable ? cache popular query
results or optimize performance based on stable
workload
20
Geographical Locality
Compare local sharing in geographical
clusters vs. in random clusters
Limited geographical locality in users browse
interest.
21
Load Distribution of Users
Offline users generate more bursty traffic ? need
to identify properly handle such bursts
22
Outline

• Motivation
• Related Work
• Overview of Data Logs and Key Results
• Detailed Analysis
• Notification Services
• Browse Services
• Correlation between the Two Services
• Summary and Implications

23
Correlation between Notification and Browsing

• Correlation in the amount of usage
• Correlation in popular content categories

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Correlation in Amount of Usage
correlation coefficient is 0.26 for all users,
and 0.12 for wireless users.
Low correlation in usage.
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Correlation in Content Categories

• Approach
• Classify notifications and browsing requests into
  content categories
• For each individual user, compare his/her top N
  notification categories with top N browsing
  categories
• Metric
• Average overlap

• Wireless users have moderate correlation in
  content.
• The correlation is much lower when considering
  all users.

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Summary Implications
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Summary Implications (Cont.)
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Comparison