Modeling roaming in large-scale wireless networks using real measurements

mashpeemoveMobile - Wireless

Nov 24, 2013 (3 years and 11 months ago)

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M. Papadopouli
1,2,3
, M. Moudatsos
1
,
M. Karaliopoulos
2

1
Institute of Computer Science, FORTH, Heraklion, Crete, Greece

2
University of North Carolina, Chapel Hill, United States

3
University of Crete, Heraklion, Crete, Greece

Modeling roaming in large
-
scale wireless
networks using real measurements

IEEE WoWMoM ’06, 1
st

EXPONWIRELESS WORKSHOP, Niagara Falls, NY, 26 June 2006

Outline


Background
-

Motivation


Wireless infrastructure


Measurement data


Describing wireless network access with
graphs


Graph definition/generation


Graph properties


Current work

Background in WLAN arena


Desire to support real
-
time services


QoS provision challenges



Integration with other wireless networks


Mobile cellular networks


Wireless backbone for other wireless nets (
e.g.
, Personal
Networks)



Standardization efforts to support/enhance
control and management
-
plane functions


IEEE 802.11k


IETF CAPWAP WG

Motivation


Real measurement data are critical to system
engineering


Understand system dynamics, traffic & user access patterns,
weaknesses & deficiencies


Derive models for the user activity and the network



input to system engineering tasks and performance analysis



Challenge
: identify trends/principles/rules that
hold independent of the specific infrastructure


Need for validation: describe findings, apply to other
datasets, compare with others’ findings





This study uses measurement data to come up
with an alternative description of the UNC
network and the user access patterns


UNC wireless infrastructure


Over 750 APs


Steady growth :460 (Oct 04), 570 (Apr 05), 640 (Sep 05),
750 (May 06)



Spread amongst over 110 buildings


Primarily academic, residential, administrative and clinical



40,000 users


10,000 different clients were logged in the traces


Clients almost exclusively laptops or PDAs

Measurement data


Relied on Syslog messages


Syslog agents activated in the APs


Server on a dedicated host collects the data


24/7 process



Syslog messages log down several events


Client (re/de)association, (de)authentications, roaming
(transition between two APs)


SYSLOG message generation

UNC Wired

Network

Wireless

Network

Router

Internet

User i

AP 1

AP 2

User D

AP3

Switch

:
SYSLOG message(s)

User j

1

time
t
1

User j association

2

time

t
2

Roaming to AP2

disconnection

time
t
4

4

time

t
3

Roaming to AP3

3

Roaming activity as a graph


AP i



湯摥 椠





Client transition between APs


敤来
between corresponding nodes


Directed graph: (
i,j
)


E when


client transition from AP
i

to AP

j


Undirected graph: (
i,j
)


E when


client transition in either
direction


Weight of edge (
i,j
) : # client transitions from AP
i

to AP

j


Function of the tracing period T, G
Τ

= (
V
T
, E
T
)


In the paper, T = 1 week


3 different weeks are studied : 17
-
24 Oct 04, 2
-
9 Mar 05,
13
-
20 Apr 05

Graph G = (V, E)

Degree of connectivity (DoC)


Distribution of degree of connectivity


Indegree, outdegree (directed graph)


Degree (undirected graph)



Goodness
-
of
-
fit tests


Visual tests (quantile
-
quantile plots)


Statistical tests



Evolution of DoC with time

DoC


visual goodness
-
of
-
fit tests


Several discrete distributions tested


Negative Binomial


Geometric


Binomial


Poisson



ML estimation of parameters for the statistical
tests




乥N慴楶i⁂楮潭楡i 杩癥猠捯湳n獴敮瑬礠瑨攠扥獴s晩琠
for all degrees and for all three periods

Indegree QQ
-
plots


week 1

Outdegree QQ
-
plots


week 1

Degree QQ
-
plots


week 2

DoC


statistical goodness
-
of
-
fit tests


Chi2
-
based and EDF
-
based tests


Pearson chi2 statistic


Kolmogorov
-

Smirnov test



Hypothesis for Geometric distribution rejected
even at 1% level



Negative Binomial:


the single distribution that passes the hypothesis testing
at
all significance levels

(1%, 5%, 10%)



Inappropriateness of the power
-
law

Degree evolution with time (1)


How does evolution of infrastructure affect the
degree of nodes?



Less clear answers…

Week

Tracing Period

Num Clients

Total APs

1

17
-
24 October 200
4

8880

459

2

2
-
9 March 2005

9049

532

3

13
-
20 April 2005

9881

574


Degree evolution with time (2)


Change of stochastic order after some degree value


Strong dependence on the location of new APs


AP additions extending coverage contribute small DoCs


APs in busier places contribute high degrees


Edge weights vs. distance (1)


similar approach : visual inspection and
statistical analysis

Scatterplot for
the three periods



Negative correlation as expected

Edge weights vs. distance (2)


Negative correlation
non
-
linear




Spearman rank correlation coefficient instead of the Pearson
product
-
moment coefficient


Hypothesis of independence between the two is
rejected at significance levels << 1%


chi2 test for independence over contingency table


chi2 statistic values = 4
-
7 times the critical
values at the 1% statistical significance level


c
1

c
2



c
j



c
p

r
1









r
2







..







r
i




# of AP
pair
s with
edge weight
in the
j
th
bin and physical distance in the i
th

bin











r
r








Edge weights

Distance of
edge APs

Graph demonstration


week 1

week 1


Oct 2004

Graph demonstration


week 2

week 2


Mar 2005

Graph demonstration

week 3

week 3


Apr 2005

Current work


Get graph “signature” for other infrastructures


First target : Dartmouth wireless network


First results show agreement in the graph degree
distribution (Neg. Binomial)



Repeat analysis for smaller time
-
scales


Down to 1 hour or 1
-
hour intervals over whole week


More interesting for system engineering functions


First results show less concise characterization


best
-
fit
distributions vary with time

UNC/FORTH
Web
Archive


Online archiving of datasets


http://www.cs.unc.edu/Research/mobile/datatraces.htm


Login/passwd access after free registration



Web repository of wireless measurement data


Packet header traces, SNMP, SYSLOG, signal quality
measurements


Joint effort between Mobile Computing Groups in UNC &
FORTH


Complements similar efforts (e.g., CRAWDAD)

WiTMemo

06

Second International workshop on Wireless
Traffic Measurements and Modeling
(WiTMeMo’06)


August 5
th
, 2006

Boston


http://www.witmemo.org