Microsoft Research1 Characterizing Alert and Browse Services for Mobile Clients Atul Adya, Victor Bahl, Lili Qiu Microsoft Research USENIX Annual Technical

Microsoft Research 1

Characterizing Alert and Browse Services for Mobile Clients

Atul Adya, Victor Bahl, Lili QiuMicrosoft Research

USENIX Annual Technical ConferenceMonterey, CA, June 2002


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


Motivation Wireless web services

Becoming popular Crucial to understand usage pattern Few existing studies on how they are used


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


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


Overview: User CategoriesCellular 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:User Type # Users # Requests

Cellular 58,432 2,210,758

Offline 50,968 20,508,272

Desktop 639,971 7,342,206

Misc. 1,634 2,944,708


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






Notification Log Analysis

Types of Analyses Content analysis Notification message popularity User behavior analysis

Geographical locality


Content Analysis

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Categories

KB

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Weekday Weekend

Important to content providers and notification service designers

Popular categories: weather, news, stock quotes, email.


Notification Message Popularity Researchers have found Web accesses follow

Zipf-like distribution (i.e., # request 1/i)

1.E+00

1.E+01

1.E+02

1.E+03

1.E+04

1.E+05

1.E+06

1 10 100 1,000 10,000 100,000 1,000,000

Popularity ranking of msg

# T

ran

sm

iss

ion

s

Trace Least square line fit

Notification message popularity follows Zipf-like distribution ( [1.1, 1.3]) generate synthetic traces


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

00.20.40.60.8

1

0 0.2 0.4 0.6 0.8 1

Fraction of notification documents

Fra

ctio

n of

tota

l m

essa

ges

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


Geographical Locality Local sharing

2 users in the same cluster receive the msg

00.10.20.30.40.50.60.70.8

0 500 1000 1500 2000 2500 3000

City ID

Fra

ctio

n o

f re

qu

est

s lo

cally

sh

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Geographical cluster Random cluster

Notification exhibits geographical locality.






Browser Log Analysis

Types of Analyses Content analysis Documents popularity User behavior analysis

Temporal stability Geographical locality Load distribution of different users


Content Analysis

Important to content providers: what content isinteresting to users

Rank #1

Rank #2 Rank #3

Notification Wireless News Weather Stock

Browse Wireless Stock quotes

News YellowPages

Offline Help News Stock

Desktop Sign-ups

Email Sports

Top three preferences for different kinds of users


Document PopularityTwo definitions of document

Base URLs Full URLs: including parameters

1.E+001.E+011.E+021.E+031.E+041.E+051.E+06

1 10 100 1000

Popularity ranking of base urls

# Re

ques

ts

1.E+001.E+01

1.E+021.E+03

1.E+041.E+05

1.E+06

1.E+00 1.E+01 1.E+02 1.E+03 1.E+04 1.E+05

Popularity ranking of full urls

# R

eque

sts

Document Popularity does not closely follow Zipf-like distribution.


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

0

0.2

0.4

0.6

0.8

1

0 0.2 0.4 0.6 0.8 1

Percentage of full urls

Pe

rce

nta

ge

of

req

ue

sts

Very small amount of memory needed to cache popular query results if content doesn’t

change.


Temporal Stability Methodology

Consider 2 days’ traces

Pick the top n documents from each day

Compute overlap0

0.2

0.4

0.6

0.8

1

1.E+00 1.E+02 1.E+04 1.E+06

# Top documents picked

Fra

ctio

n o

f o

verl

ap

8/15 vs. 8/16 8/15 vs. 8/17 8/15 vs. 8/188/15 vs. 8/19 8/15 vs. 8/20

Popular urls remain stable cache popular query results or optimize performance based on stable workload


Geographical Locality

Wireless Users

0

0.1

0.2

0.3

0.4

0 100 200 300

City ID

Fra

ctio

n o

f re

qu

est

s lo

cally

sh

are

d

Geographical Random 1 Random 2Random 3 Random 4

Limited geographical locality in users’ browse interest.

Compare local sharing in geographical clustersvs. in random clusters


Load Distribution of Users

0

20

40

60

80

100

1.E+00 1.E+01 1.E+02 1.E+03 1.E+04 1.E+05 1.E+06

Interarrival time (seconds)

Per

cent

age

of

requ

ests

Offline users Wireless users

Offline users generate more bursty traffic need to identify & properly handle such bursts






Correlation between Notification and Browsing

Correlation in the amount of usage Correlation in popular content

categories


Correlation in Amount of Usage

1

10

100

1000

0 200 400 600 800

# browse requests from a user

avg

. # n

otif

ica

tion

s to

a

use

r

1

10

100

1000

0 200 400 600 800

# notifications to a userA

vg #

bro

wse

re

qu

est

s fr

om

a u

ser

Low correlation in usage.

correlation coefficient is 0.26 for all users, and 0.12 for wireless users.


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

0

10

20

30

40

50

60

1 2 3 4 5 6 7 8 9N

Ave

rag

e a

mo

un

t o

f o

verl

ap

(%

)

All Users Wireless Users

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


Summary & Implications

Observations Implications

Top 1% notification objects account for 54-64% of total messages.

Delivering notifications via multicast would be effective.

Notification exhibits geographical locality.

Useful to provide localized notification services.


Summary & Implications (Cont.)

Observations Implications

0.1% - 0.5% full urls (i.e. 121-442) account for 90% requests.

Caching the results of popular queries would be very effective.

The set of popular urls remain stable.

Cache a stable set of popular query results or optimize query performance based on a stable workload.

Limited correlation between users’ browsing and notification pattern.

Service providers cannot solely rely on users’ notification profile to predict how much & what they will browse.


Comparison

Notification BrowsingZipf-like popularity distribution

Yes No

High concentration of msgs/requests to documents

Yes Yes

Spatial Locality Significant Little

Correlation Limited correlation in both usage and content