Anjum Naveed

Modeling Per-flow Throughput and Capturing
Starvation in CSMA Multi-hop Wireless Networks
M. Garetto, T. Salonidis, E. W. Knightly
Rice University, Houston, TX, USA
IEEE Infocom’06
Sequence of Presentation
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2
The domain
Paper Composition
Approach used in paper
Throughput modeling
Simulation Results
Study relating starvation – I will not discuss this part
Related work
Discussion - Space for future work
The domain
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3
Multi-hop wireless Networks
Capacity of Multi-hop wireless networks.
Not the asymptotic bounds like Gupta &
Kumar
Link Level throughput - End-to-end
Throughput
Probabilistic approach
Modeling using Markov Chains
Paper Composition
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Ideas reused/enhanced from:
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–
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4
Giuseppe Bianchi, “Performance Analysis of IEEE
802.11 DCF”, JSAC, march 2000
Robert R. Boorstyn et al., “Throughput Analysis in
Multi-hop CSMA Packet Radio Networks”, IEEE
Transactions on Communications, March 1987
Authors work for 2 flow modeling – A probabilistic
model developed for the work in this paper.
Approach used in Paper
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5
The probabilistic model developed based on the
behavior of CSMA protocol
Per link saturated state throughput computed using
above model for all links in the network
Model extended for non-saturated case using queue
information.
Simulation based experimental validation of model.
Issue of link level starvation considered.
Throughput Modeling
σ
Ts
Tc
Tb
σ
t
 (1  p)
Tp 
 (1  p)Ts  pTc  (1   )(1  b)  (1   )bTb

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Unknowns, b, Tb and p. Different for every node, depending
upon its location and location of interfering nodes
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6
2(1  2 p)
(1  2 p)(W  1)  pW (1  (2 p) m )
Backup slide if required for IA and FIM
Throughput Modeling
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Computation of b(i) and Tb(i) for
a given station i assuming
behavior of all other stations is
known
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Finding active regions
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7
2
1
Definition of active region – where
nodes have same behavior as seen
by ‘i’
Find all maximal cliques which ‘i’ is
part of.
Find minimum number of maximal
cliques
5
3
i
4
6
Empty
region
Throughput Modeling
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For a Given node ‘j’, let Ton(j) be average
active duration and λ(j) be on event
generation rate.
–
For one active region ‘U’ λ(U)=ΣjЄU λ(j)

(U ) 
Ton
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8
jU
 ( j )Ton( j )
 (U )
Markov model - Activation rate of virtual node
(active region) gu and deactivation rate μu=1/Ton(u)
Throughput Modeling

Let ‘D’ be independent set of
virtual nodes, i.e., {3,5}

gu 
Q( )   
allD uD u 

gu 
Q( D)    Q( )
 uD u 
idle(i)  u gu
Tidle(i)  1
Tidle(i)1  Q( )
Tb(i) 
Q( )
  (u) 
9
u
ne(i ) 
1
u
 (u )
Tidle(i)  Tb(i)
[1   (i)]b(i)ne(i)
(i)
3
1
idle(i)

2
4
5
6
Throughput Modeling
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Computation of ‘p(i)’
p(i)  1  1  pco(i)1  pia(i)1  pnh(i)1  pfh(i)
Q ( )
c(i ' | i ) 
DA(i ) Q( D)
1

Ton  Toff

Pco (i, i ' )  c(i ' | i ) (i ' )
d

Toff
pia(i, i ' )  1 
e Toff
Ton  Toff
Q( D)
DA ( i )
pnh(i, i' )  c(i' | i) 1  1   (i' ) 
10
A
c’
 (i ' )

B
m

Ton
pfh(i, i' ) 
Ton  Toff
c
a
C
D
d
Simulation results
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Conclusions from Simulation Results
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Major source of Loss is not CO which most of the work
analyzes
Major loss is due to IA, NH and FH
Which one causes most loss? - FH, NH, IA
With perspective of single flow, IA, FH, NH
Starvation is direct consequence of IA and FIM
With CSMA, few links capture the channel for most of time
while others suffer badly
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Network throughput is not a good metric as considered by
many capacity papers.
Related work
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Boorstyn [80-87]
– Modeled behavior of CSMA using markov chains. Authors have
used same modeling
Medepalli et al. [infocom06]
– Extending model of Boorstyn et al. and Bianchi.
– Focusing on role of back off and contention window like Bianchi
– Do not consider dependencies problem
Kashyap, Ganguly & S. R. Das [Mobicom’07]
– More practical measurement based & probabilistic approach
– Do not consider dependencies problem.
– Validated model for small networks only.
These are different from capacity work where bounds are calculated.
These are more accurate and fine tuned in my understanding
Discussion – Space for future work
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13
Reduce complexity - Make model work practically
Improve accuracy by considering physical layer
features
Assumption of exponential distribution to be
relaxed/changed
Suggestions for changes in parameters, like bianchi
suggested adjusting values of W and m according to
network size
Further investigation of IA, NH and FH to quantify the
loss probability
Conclusion
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14
Detailed and proper modeling
Improved writing and better organization of
paper would have helped a lot
The Model can be used as basis for channel
assignment techniques
QUESTIONS
?
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AI and FIM
B
A
16
a
a
b
c
A
B
C
b
Simulation Results
17
Simulation Results
18
Link Dependencies example
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Change in demand of link Dd affects the link Aa,
several hops away and out of career sensing range
D
C
B
A
19
a
b
c
d

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