m. papadopouli 1,2,3, m. moudatsos 1, m. karaliopoulos 2 1 institute of computer science, forth,...
TRANSCRIPT
M. Papadopouli1,2,3, M. Moudatsos1, M. Karaliopoulos2
1Institute of Computer Science, FORTH, Heraklion, Crete, Greece2University of North Carolina, Chapel Hill, United States
3University of Crete, Heraklion, Crete, Greece
Modeling roaming in large-scale wireless networks using real measurements
IEEE WoWMoM ’06, 1st 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
AP3Switch
: SYSLOG message(s)
User j
1
time t1
User j association
2
time t2
Roaming to AP2
disconnection
time t4
4
time t3Roaming to AP3
3
Roaming activity as a graph
AP i node i V
Client transition between APs edge 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, GΤ = (VT, ET)
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
Negative Binomial gives consistently the best fit 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 2004 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
c1 c2 … cj … cp r1 r2 .. ri # of AP pairs with edge weight in the
jth bin and physical distance in the ith bin
… rr
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 5th, 2006
Boston
http://www.witmemo.org