IEEE Infocom’06
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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
description
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. The domain Paper Composition Approach used in paper Throughput modeling - PowerPoint PPT Presentation
Transcript of IEEE Infocom’06
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
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Sequence of Presentation
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
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The domain
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
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Paper Composition
Ideas reused/enhanced from:– 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.
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Approach used in Paper
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.
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Throughput Modeling
Unknowns, b, Tb and p. Different for every node, depending upon its location and location of interfering nodes
Backup slide if required for IA and FIM
Ts Tc Tbσt
σ
bcsp
bTbpTTp
pT
)1()1)(1()1(
)1(
τσττττ
−+−−++−−
=
))2(1()1)(21(
)21(2mppWWp
p
−++−−
=τ
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Throughput Modeling
Computation of b(i) and Tb(i) for a given station i assuming behavior of all other stations is known
– Finding active regions 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
i
Empty region
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54
321
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Throughput Modeling
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)
– Markov model - Activation rate of virtual node (active region) gu and deactivation rate μu=1/Ton(u)
)(
)()()(
U
jTjUT Uj
on
onλ
λ∑ ∈=
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Throughput Modeling
Let ‘D’ be independent set of virtual nodes, i.e., {3,5} 1
4
2 3
5
6
)()( ϕμ
Qg
DQDu u
u
⎟⎟⎠
⎞⎜⎜⎝
⎛= ∏
∈
1
)(−
∈⎥⎦
⎤⎢⎣
⎡= ∑∏
allD Du u
ugQ
μϕ
∑=u
uidle gi)(λ )(1)( iiT
idleidle λ=
[ ])(
)(1)()(
ϕ
ϕ
Q
QiTiT
idleb
−= [ ])()(
)()(
iTiT
uin
bidle
ue
+=∑ λ
)(
)()()](1[)(
i
inibiu
e
u Δ−
=∑ τλ
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Throughput Modeling
Computation of ‘p(i)’
A
a
B
c C
c’
d
D
[ ][ ][ ][ ])(1)(1)(1)(11)( ipipipipip fhnhiaco −−−−−=
∑ ∈
=)(
)(
)()|'(
iADDQ
Qiic
ϕ )'()|'()',( iiiciiPco τ=
∑ ∈
=+
)()(
)'(1
iADoffon DQ
i
TT
λ Toff
d
offon
offia e
TT
Tiip
−
+−=1)',(
( )[ ]mnh iiiciip )'(11)|'()',( τ−−=offon
onfh
TT
Tiip
+=)',(
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Simulation results
Conclusions from Simulation Results– 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 Network throughput is not a good metric as considered by
many capacity papers.
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Related work
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
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Discussion – Space for future work
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
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Conclusion
• 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
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QUESTIONS
?
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AI and FIM
A
a
B C
cb
A a
bB
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Simulation Results
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Simulation Results
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Link Dependencies example
a
bB
c
dD
A
C
Change in demand of link Dd affects the link Aa, several hops away and out of career sensing range
