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A Fair and Dynamic Load Balancing Mechanism
F. Larroca and J.L. Rougier
International Workshop on Traffic Management and Traffic Engineering for the Future Internet
Porto, Portugal, 11-12 December, 2008
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Agenda
IntroductionUtility Maximization Load-BalancingDistributed AlgorithmSimulations
• Packet-Level Simulations• Fluid-Level Comparison
Conclusions
F. Larroca and J.L. Rougier FITRAMEN 08, Dec. 2008
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Introduction
Network Convergence:• Traffic increasingly unpredictable and dynamic
Classic TE techniques (i.e. over-provisioning) inadequate: • Ever-increasing access rates• New emerging architectures with low link capacities
Possible answer: Dynamic Load-Balancing• Origin-Destination (OD) pairs with several paths:
how to distribute its traffic?• Paths configured a priori and distribution dependent
on current TM and network condition
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Introduction
Network operator interested OD pairs obtained performance• Why not state the problem in their terms?
Analogy with Congestion Control (TCP):• End-hosts = OD pairs• Rate = OD performance indicator
Differences: • Decision variable: portion of traffic sent through each
path (total traffic is given)• Much larger time-scale
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Introduction
Previous proposals: • Define a link-cost function ll for each link l=1..L• Minimize the total network’s cost
Limitations: • Indirect way of proceeding• Cannot prioritize an OD pair or enforce fairness
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Example:
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Agenda
IntroductionUtility Maximization Load-BalancingDistributed AlgorithmSimulations
• Packet-Level Simulations• Fluid-Level Comparison
Conclusions
F. Larroca and J.L. Rougier FITRAMEN 08, Dec. 2008
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Utility Maximization Load-Balancing
Define a single performance indicator per OD pair• us(d): performance perceived by OD pair s when
traffic distribution is d “Distribute” us(d) among OD pairs to maximize total
Utility (à la Congestion Control)
• ds = total demand of OD pair s (given)• dsi = traffic sent through path i of OD pair s (∑dsi= ds)• d = [ d11 d12 .. dS1 .. dSnS ]T
How to define us(d)?
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S
ssss
dduUd
1
))((max
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Utility Maximization Load-Balancing
Our choice for us(d): mean path’s Available Bandwidth (ABW)
Assumptions: • Majority of traffic is elastic (i.e. TCP)• Path choice considered propagation delay
Advantages: • Mean ABW rough approximation of rate obtained by
TCP flows (ABW is the most important indicator)• Sudden increases in demand may be
accommodated
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lsil
sisisis ABWpABWp(d)u minarg
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Utility Maximization Load-Balancing
Final version of the problem:
If ABWsi is the flow obtained rate, the problem is very similar to Multi-Path TCP• By only changing ingress routers, users may be
regarded as if they used MP-TCP: improved performance and more supported demands
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s
s
n
issi
S
s
n
isisiss
d
sdddcRdts
ABWpUd
1
1 1
,0,..
max
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Agenda
IntroductionUtility Maximization Load-BalancingDistributed AlgorithmSimulations
• Packet-Level Simulations• Fluid-Level Comparison
Conclusions
F. Larroca and J.L. Rougier FITRAMEN 08, Dec. 2008
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Distributed Algorithm
The optimization problem is not convex However, not too “unconvex” The distributed algorithm solves the dual problem
and results in a good approximation Based on the Harrow-Hurwitz method: greedy on
path utility (PU) minus path cost (PC)
page 11 F. Larroca and J.L. Rougier FITRAMEN 08, Dec. 2008
otherwise ,0
minarg if ),('
and ˆ where
ˆ)('
:
lsil
lsi
sil
s silisill
sillsisissi
ABWlABWUd
PCABWuUPU
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Agenda
IntroductionUtility Maximization Load-BalancingDistributed AlgorithmSimulations
• Packet-Level Simulations• Fluid-Level Comparison
Conclusions
F. Larroca and J.L. Rougier FITRAMEN 08, Dec. 2008
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Packet-Level Simulations
A simple example: all links have the same capacity and probabilities are updated every 50 seconds
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Comparison with two previous proposals: • MATE: minimize total M/M/1 delay
• TeXCP: greedy on the path’s maximum utilization
Two performance indicators: • Mean ABW (us) (weighted mean, 10% quantile and
minimum)• Link Utilization (mean, 90% quantile and maximum)
Fluid-Level Simulations
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In two real topologies and TMs:
l lABW
1min
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Mean ABW (us)
Link Utilization
UM/MATE
Fluid-Level Simulations – Abilene
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UM/TeXCP
TeXCP - MATE TeXCP - UM
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Fluid-Level Simulations – Géant Mean ABW (us)
Link Utilization
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UM/MATE UM/TeXCP
TeXCP - MATE TeXCP - UM
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Agenda
IntroductionUtility Maximization Load-BalancingDistributed AlgorithmSimulations
• Packet-Level Simulations• Fluid-Level Comparison
Conclusions
F. Larroca and J.L. Rougier FITRAMEN 08, Dec. 2008
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Conclusions
Performance as perceived by OD pairs is always better in UM than in MATE or TeXCP• MATE: relatively small differences in mean, but
significant in the worst case• TeXCP: more significant differences
Link utilization results for TeXCP and UM are very similar• MATE: although similar in mean and quantile, the
maximum link utilization may increase significantly Future Work:
• Stability• Other simpler methods or objective function that obtains
similar results
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Thank youQuestions?
F. Larroca and J.L. Rougier FITRAMEN 08, Dec. 2008