Microsoft Research1 Characterizing Alert and Browse Services for Mobile Clients Atul Adya, Victor Bahl, Lili Qiu Microsoft Research USENIX Annual Technical Conference Monterey, CA, June 2002

Microsoft Research2 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

Microsoft Research3 Motivation Wireless web services Becoming popular Crucial to understand usage pattern Few existing studies on how they are used

Microsoft Research4 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 Only 80K requests over seven months No existing study on notification usage

Microsoft Research5 Overview A popular commercial Web site for mobile clients Content news, weather, stock quotes, , yellow pages, travel reservations, entertainment etc. Services Notification Browse Period studied 3.25 million notifications in Aug. 20 – 26, million browse requests in Aug. 15 – 26, 2000

Microsoft Research6 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: User Type# Users# Requests Cellular 58,432 2,210,758 Offline 50,96820,508,272 Desktop639,971 7,342,206 Misc. 1,634 2,944,708

Microsoft Research7 Major Findings Notification Services Popularity of notification messages follows Zipf-like distribution Top 1% notification objects account for % 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

Microsoft Research8 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

Microsoft Research9 Notification Log Analysis Types of Analyses Content analysis Notification message popularity User behavior analysis Geographical locality

Microsoft Research10 Content Analysis Important to content providers and notification service designers Popular categories: weather, news, stock quotes, .

Microsoft Research11 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

Microsoft Research12 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.

Microsoft Research13 Geographical Locality Local sharing  2 users in the same cluster receive the msg Notification exhibits geographical locality.

Microsoft Research14 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

Microsoft Research15 Browser Log Analysis Types of Analyses Content analysis Documents popularity User behavior analysis Temporal stability Geographical locality Load distribution of different users

Microsoft Research16 Content Analysis Important to content providers: what content is interesting to users Rank #1Rank #2Rank #3 NotificationWirelessNewsWeatherStock BrowseWirelessStock quotes NewsYellowPages OfflineHelpNewsStock DesktopSign-ups Sports Top three preferences for different kinds of users

Microsoft Research17 Document Popularity Two definitions of document Base URLs Full URLs: including parameters Document Popularity does not closely follow Zipf-like distribution.

Microsoft Research18 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 doesn’t change.

Microsoft Research19 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

Microsoft Research20 Geographical Locality Limited geographical locality in users’ browse interest. Compare local sharing in geographical clusters vs. in random clusters

Microsoft Research21 Load Distribution of Users Offline users generate more bursty traffic  need to identify & properly handle such bursts

Microsoft Research22 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

Microsoft Research23 Correlation between Notification and Browsing Correlation in the amount of usage Correlation in popular content categories

Microsoft Research24 Correlation in Amount of Usage Low correlation in usage. correlation coefficient is 0.26 for all users, and 0.12 for wireless users.

Microsoft Research25 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.

Microsoft Research26 Summary & Implications ObservationsImplications Top 1% notification objects account for % of total messages. Delivering notifications via multicast would be effective. Notification exhibits geographical locality. Useful to provide localized notification services.

Microsoft Research27 Summary & Implications (Cont.) ObservationsImplications 0.1% - 0.5% full urls (i.e ) 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.

Microsoft Research28 Comparison NotificationBrowsing Zipf-like popularity distribution YesNo High concentration of msgs/requests to documents Yes Spatial LocalitySignificantLittle CorrelationLimited correlation in both usage and content