UNIVERSITY OF MASSACHUSETTS, AMHERST • 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.

Outline

Introduction Measurement Methodology Measurement Analysis and

Findings Empirical Investigation of

Model Conclusion

2

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

Mobility in, and among, among networks

Physical mobility among access points

4

mobileuser

visitednetwork

Mobile Switching

Center

VLR

Cellular network mobility (e.g., [3])

toInternet

[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

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 networkEnterprise

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]

[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

Outline

Introduction Measurement Methodology Measurement Analysis and

Findings Empirical Investigation of

Model Conclusion

7

8

How to get traces of virtual mobility?

…

………

…

…

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.

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

ASes in decreasing order of the fraction of Sign-in logs

Fra

cti

on

of

Sig

n-i

n log

s

(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

Alice made Comcast connections

time∆t ∆t ∆t ∆t ∆tt1 t2 t3 t4 t5 t6

Alice has been connected to Comcast from t1 to t3.

Alice has been connected to Verizon from t2 to t3

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 Knee (elbow) at 15mins!

Nu

mb

er

of

sessio

ns

(X10

6)

[7] J. Padhye and J. F. Kurose. Continuous-media courseware server: A study of client interactions. IEEE Internet Computing, 1999

Outline

Introduction Measurement Methodology Measurement Analysis and

Findings Empirical Investigation of

Model Conclusion

13

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 usersCS users

40%70%

15

Network residence time (over all users)

80-to-90% from three categories only with “8” ASes out of 400

Five colleges (incl. UMass)

HOUSE

WORK MOBILE

Comcast cableVerizon online

Charter communicationsHughes 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.

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.

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

Most contemporaneous users spent up to 20% of their connection time in multiple networks.

UMass-wide users

Contemporaneous usersSing

le

conn

ection

use

r80% of CS users

50% of UMass-wide users

Outline

Introduction Measurement Methodology Measurement Analysis and

Findings Empirical Investigation of

Model Conclusion

19

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

Signalingoverhead at time t

User’s network transition

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(Xt, Yt)-series data properties

Investigate stationary, memoryless properties

Time series plot on a daily value of Yt (all users)

KPSS test: data stationarity

Autocorrelation function (ACF): daily/weekly periodicity

Model estimation(phase 1)

Model validation(phase 2)

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Signaling overhead over all users

Visually a good fit

model (phase 1)observed (phase 2)

CS users signaling overhead

How well does the model predict signaling overhead?

Statistically a good fit

Q-Q plot

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UMass-wide signaling overhead

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.

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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.

UNIVERSITY OF MASSACHUSETTS, AMHERST • School of Computer Science

End

Questions or comments welcome!