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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
This research is supported by US NSF awards CNS-1040781 and CNS-1345300.
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Outline
Introduction Measurement Methodology Measurement Analysis and
Findings Empirical Investigation of
Model Conclusion
2
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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
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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
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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
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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
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Outline
Introduction Measurement Methodology Measurement Analysis and
Findings Empirical Investigation of
Model Conclusion
7
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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!
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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.
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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)
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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
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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
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Outline
Introduction Measurement Methodology Measurement Analysis and
Findings Empirical Investigation of
Model Conclusion
13
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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%
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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
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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.
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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”
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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
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Outline
Introduction Measurement Methodology Measurement Analysis and
Findings Empirical Investigation of
Model Conclusion
19
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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.
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UNIVERSITY OF MASSACHUSETTS, AMHERST • School of Computer Science
End
Questions or comments welcome!