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 Microsoft Research

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 Microsoft Research

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

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 Microsoft Research

5. 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 Microsoft Research

6. 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: Microsoft Research

7. 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 Microsoft Research

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 Microsoft Research

9. Notification Log Analysis Types of Analyses • Content analysis • Notification message popularity • User behavior analysis • Geographical locality Microsoft Research

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

11. 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 Research

12. 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 Research

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

14. 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 Research

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

16. Content Analysis Important to content providers: what content is interesting to users Top three preferences for different kinds of users Microsoft Research

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

18. 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 Research

19. 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 Research

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

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

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 Microsoft Research

23. Correlation between Notification and Browsing • Correlation in the amount of usage • Correlation in popular content categories Microsoft Research

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

25. 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 Research

26. Summary & Implications Microsoft Research

27. Summary & Implications (Cont.) Microsoft Research

28. Comparison Microsoft Research