1. U NIVERSITY OF M ASSACHUSETTS, A MHERST School of Computer Science Measurement and Modeling of User Transitioning among Networks Sookhyun Yang, Jim Kurose, Simon Heimlicher, and Arun Venkataramani University of Massachusetts Amherst shyang@cs.umass.edu This research is supported by US NSF awards CNS-1040781 and CNS-1345300.

2. Outline Introduction Measurement Methodology Measurement Analysis and Findings Empirical Investigation of Model Conclusion 2

3. Mobility is the key driver of networking 3 Historic shift from PC’s to mobile/embedded devices INTERNET (2020) INTERNET (2020) ~ 2B server/PC’s ~ 10B mobiles ~ 1B server/PC’s ~ 1B smartphones INTERNET (2011) INTERNET (2011) ~ 1B Internet -connected PC’s ~ 5B cell phones [1] Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2014–2019 [2] Pew Research Center, The Internet of Things Will Thrive by 2025, 2014

4. Mobility in, and among, among networks Physical mobility among access points 4 mobile user visited network Mobile Switching Center VLR Cellular network mobility (e.g., [3]) to Internet [4] M. Kim et al, Extracting a mobility model from real user traces, INFOCOM 2006 Wi-Fi network mobility (e.g., [4]) Device mobility within the same type of a network [3] U. Paul et al, Understanding traffic dynamics in cellular data networks, INFOCOM 2011

5. Mobility in, and among, among networks Virtual mobility among access networks Move among edge and provider networks Persistently keep his/her ID (name) across networks For instance, a stationary user with multi-homing, multiple devices 5 Cable network Cellular network Enterprise network via VPN

6. Our contribution Quantitative understanding of virtual mobility Sequence of associated networks Network residence time Degree of multi-homing Network transition rate Gives insights and implications on location- independent architectures e.g., Mobile IP, MobilityFirst [5], XIA [6] 6 [5] A. Venkataramani, J. Kurose, D. Raychaudhuri, K. Nagaraja, M. Mao, and S. Banerjee. Mobilityfirst: A mobility-centric and trustworthy internet architecture. ACM CCR, 2014 [6] D. Han et al. XIA: Efficient support for evolvable internetworking. USENIX NSDI, 2012

7. Outline Introduction Measurement Methodology Measurement Analysis and Findings Empirical Investigation of Model Conclusion 7

8. How to get traces of virtual mobility? 8 … … … … … … Large population of users! Difficult to install SW on all their devices! Question: What is the most feasible way to capture such user’s virtual mobility? Far too many servers and application servers to be monitored!

9. Can we log virtual mobility via mail server? User frequently accesses his/her mailboxes mail periodically pushed (e.g., every 5mins) to user Same user ID is used across multiple networks and sessions. Mail server logs allow us to identify the network address where a user is resident. IMAP mail access server logs Contain sign-in logs with user ID, IP address, and timestamp Informal lower-bound of the actual amount of network-transitioning performed. 9

10. IMAP mail access logs CS-only users IMAP servers for UMass School of CS 81 users, one year 405 IP prefixes, 387 ASes UMass-wide users Servers for all UMass students (primarily), faculty, and staff 7,137 users, 4 months 9,016 IP prefixes, 1,777 ASes 10 ASes in decreasing order of the fraction of Sign-in logs Fraction of Sign-in logs (e.g., Comcast cable, Verizon, Five colleges network incl. UMass, AT&T Wireless, Sprint Wireless)

11. How to reconstruct a user’s session? Given a series of IMAP sign-in logs, Time window At least one log for a time window indicates that a user is connected for the entire time window 11 Alice made Comcast connections time ∆t t1t1 t2t2 t3t3 t4t4 t5t5 t6t6 Alice has been connected to Comcast from t 1 to t 3. Alice has been connected to Verizon from t 2 to t 3 contemporaneously. Alice made Verizon connections

12. Appropriate size of a time window? Time window dilemma in session identification [7] Small window overestimates Large window underestimates # of sessions as a function of time window sizes 12 Knee (elbow) at 15mins! Number of sessions (X10 6 ) [7] J. Padhye and J. F. Kurose. Continuous-media courseware server: A study of client interactions. IEEE Internet Computing, 1999

13. Outline Introduction Measurement Methodology Measurement Analysis and Findings Empirical Investigation of Model Conclusion 13

14. Mobility among networks Approx. 70% of CS users (or 40% of UMass-wide users) moves among networks at least once a day. How frequently does a user switch a network in 15mins? 14 Daily number of a user’s mobility among ASes UMass-wide users CS users 40% 70%

15. Network residence time (over all users) 80-to-90% from three categories only with “8” ASes out of 400 15 Five colleges (incl. UMass) HOUSE WORK MOBILE Comcast cable Verizon online Charter communications Hughes network Verizon Wireless AT&T Wireless Sprint Wireless HOUSE WORK MOBILE

16. An individual user’s network residence time? Overall, users spent more than 60% of their time in their top three networks. 16 Fraction of a user’s top three networks (ASNs) residence time (%) 75% of users spent more than 90% of their time in their top three networks.

17. Contemporaneous connections (picture of my advisor’s house) 17 In the traces, a series of sign-in logs produced from “multiple” networks in 15mins implies “contemporaneous connectivity”

18. 0 Fraction of a user’s contemporaneous time to connection time (%) UMass-wide users CS users User’s contemporaneous connections 18 Most contemporaneous users spent up to 20% of their connection time in multiple networks. UMass-wide users Contemporaneous users Single connection user 80% of CS users 50% of UMass-wide users

19. Outline Introduction Measurement Methodology Measurement Analysis and Findings Empirical Investigation of Model Conclusion 19

20. User virtual mobility model Characterizes the transition rate at which a user moves among networks Predicts signaling overhead to the name and location translation service e.g., a home agent, GNS in MobilityFirst User model via a discrete-time Markov-chain : # of networks newly attached at time t, w.r.t. time t-1 : # of networks connected at t 19 Attachment signaling Detachment signaling Signaling overhead at time t User’s network transition

21. (X t, Y t )-series data properties Investigate stationary, memoryless properties Time series plot on a daily value of Y t (all users) KPSS test: data stationarity Autocorrelation function (ACF): daily/weekly periodicity 21 Model estimation (phase 1) Model validation (phase 2)

22. Signaling overhead over all users Visually a good fit model (phase 1) observed (phase 2) CS users signaling overhead 22 How well does the model predict signaling overhead? Statistically a good fit Q-Q plot

23. UMass-wide signaling overhead 23 No fit! But a mixture of Gaussian distributions. Signaling overhead Heavy user cluster of 721users Visually a better fit Signaling overhead EM clustering These results suggest proper clustering can improve the model’s signaling overhead predictability.

24. Conclusions We performed a measurement study of user virtual mobility and discussed insights and implications from the measurements. Users spend most of their time in a few networks. Large number of users are contemporaneously connected to more than one networks. We show the predictability of overall signaling overhead using an individual user model. More generally, we believe that this paper is an important step in deepening the understanding of managing virtual mobility at global scale. 24

25. U NIVERSITY OF M ASSACHUSETTS, A MHERST School of Computer Science End Questions or comments welcome!