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Optimal Multi-Path Routing and Bandwidth Allocation under Utility Max-Min Fairness
Jerry Chou and Bill LinUniversity of California, San Diego
IEEE IWQoS 2009Charleston, South CarolinaJuly 13-15, 2009
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Basic Max-Min Fair Allocation Problem
• Motivation: Bandwidth allocation is a common problem in several network applications
• Example:
A D
10 10
B1010
C
C1: AD C2: BD C3: CD
C1 C2 C3
5
Maxincrease
SaturatedflowsFully allocated link
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Utility Max-Min Fairness
100/)( 21 rr 100/)12()( 2
2 rrr 100/)403()(3 rrC1: AD C2: BD C3: CD
0 BW 10
utility
1
0
utility
1
0
utility1
00 BW 10 0 BW 10
Path of C1 Allocation Utilities
ABD (5, 5, 10) (0.25, 0.85, 0.70)
Utility functions capture differences in benefitsfor different commodities
A D
10 10
B1010
C
5
Utility Max-Min Fairness
100/)( 21 rr 100/)12()( 2
2 rrr 100/)403()(3 rrC1: AD C2: BD C3: CD
0 BW 10
utility
1
0
utility
1
0
utility1
00 BW 10 0 BW 10
A D
10 10
B1010
C
Path of C1 Allocation Utilities
ABD (5, 5, 10) (0.25, 0.85, 0.70)
ABD (6.8, 3.2, 10) (0.47, 0.47, 0.70)
Utility functions capture differences in benefitsfor different commodities
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Utility Max-Min Fairness
100/)( 21 rr 100/)12()( 2
2 rrr 100/)403()(3 rrC1: AD C2: BD C3: CD
0 BW 10
utility
1
0
utility
1
0
utility1
00 BW 10 0 BW 10
A D
10 10
B1010
C
Path of C1 Allocation Utilities
ABD (5, 5, 10) (0.25, 0. 85, 0.70)
ABD (6.8, 3.2, 10) (0.47, 0.47, 0.70)
Multi-path (8, 4, 8) (0.64, 0.64, 0.64)
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Freedom of choosing multi-path routing achieveshigher min utility and more fair allocation
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Prior Work
• Utility max-min fair allocation only considered fixed (single-path) routing
• Optimal multi-path routing only considered weighted max-min and max-min fairness
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Why is the Problem Difficult?
• Why is optimal multi-path routing and allocation under utility max-min fairness difficult?
→ Unlike conventional fixed (single) path max-min fair allocation problems1. Cannot assume a commodity is saturated just
because a link that it occupies in the current routing is full
2. Once a commodity is saturated, cannot assume its routing is fixed in subsequent iterations
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Example
• At iteration i, suppose we route both flows AD and AE with 5 units of demand
AD:5
AE:5
If routing is fixed after iteration, AD would be at most 5
A D
10/10 5/10
B
0/10
CE
5/5
0/10
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Example
• At iteration i+1, suppose we want to route AD with 10 units of demand
AD:10
AE:5
Route of AD must change to increase
A D
5/10 0/10
B
10/10
CE
5/5
10/10
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Outline
• Problem
• Approach– OPT_MP_UMMF– ε-OPT_MP_UMMF
• Application to optical circuit provisioning
• Summary
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OPT_MP_UMMF
• Step 1: Find maximum common utility that can be achieved by all unsaturated commodities
• Step 2: Identify newly saturated commodities
• Step 3: Assign the utility and allocation for each newly saturated commodity
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Key Differences
• A commodity is truly saturated only if its utility cannot be increased by any feasible routing – Requires testing each commodity for saturation
separately
• To guarantee optimality, fix the utility, not the routing after each iteration
Fix utility,not routing
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Comments• Although OPT_MP_UMMF achieves optimal
solution, both Steps 1 & 2 require solving non-linear optimization problems
Step 1 Step 2
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ε-OPT_MP_UMMF
• Instead of solving a non-linear optimization problem, find maximum common utility by means of binary search
• Test if a common utility has feasible multi-path routing by solving a Maximum Concurrent Flow (MCF) problem
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Maximum Concurrent Flow (MCF)
• Given network graph with link capacities and a traffic demand matrix T, find multi-path routing that can satisfy largest common multiple of T
• If < 1, means demand matrix cannot be satisfied
• If > 1, means bandwidth allocation can handle more traffic than specified demand matrix
• MCF well-studied with fast solvers
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Find Maximum Utility• Determine demand matrix by utility functions• Find feasible routing by querying MCF solver
– If <1, decrease utility, otherwise increase utility
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2010 30 40 50
4060
80
100
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2010 30 40 50
4060
80
100
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2010 30 40 50
4060
80
100
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2010 30 40 50
4060
80
100
BW BW BW BW
Utility(%
)
Utility(%
)
Utility(%
)
Utility(%
)
C = 100Max utility Traffic (T)
1 (50,50,50,50) 0.5
.0.6±ε (10,40,10,40) 1
0.5 (10,30,10,40) 1.25
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Optical Circuit Provisioning Application• Provision optical circuits for Ingress-Egress (IE) pairs
to carry aggregate traffic between them
• Goal is to maximize likelihood of having sufficient circuit capacity to carry traffic
WDM links
Optical circuit-switched long-haul backbone cloud
Boundaryrouters
Opticalcircuit switches
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Optical Circuit Provisioning (cont’d)• Utility curves are Cumulative Distribution Functions
(CDFs) of “Historical Traffic Measurements”
• Maximizing likelihood of sufficient capacity by maximizing utility functions
• Route traffic over provisioned circuits by default
• Adaptively re-route excess traffic over circuits with spare capacity
• Details can be found in– Jerry Chou, Bill Lin, “Coarse Circuit Switching by Default,
Re-Routing over Circuits for Adaptation”, Journal of Optical Networking, vol. 8, no. 1, Jan 2009
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Experimental Setup
• Abilene network– Public academic network– 11 nodes, 14 links (10 Gb/s)
• Historical traffic measurements– 03/01/4 – 04/21/04
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Example
Seattle
Sunnyvale
Indianapolis
Denver
Los Angeles Kansas City
Chicago New York
Washington
Atlanta
Houston
SeattleNY:90% time ≤ 6Gb/s50% time ≤ 4Gb/sAllocate: 6Gb/s
SunnyvaleHouston:90% time ≤ 6Gb/s80% time ≤ 4Gb/sAllocate: 4Gb/s
Seattle NY has 90% acceptance probabilitySunnyvale Houston has 80% acceptance probability
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Comparison of Allocation Algorithms• WMMF: Single-path weighted max-min fair
allocation– Use historical averages as weights– Only consider OSPF path
• UMMF: Single-path utility max-min fair allocation– Only consider OSPF path
• MP_UMMF: Multi-path utility max-min fair allocation– Computed by our algorithm
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Individual Utility Comparison• Reduce link capacity to 1 Gb/s• MP_UMMF has higher utility for most flows
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Minimum Utility Comparison • MP_UMMF has greater minimum utility
improvement under more congested network
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Excess Demand Comparison
• Simulate traffic from 4/22/04-4/26/04• MP_UMMF has much less excess demand
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Summary of Contributions
• Defined multi-path utility max-min fair bandwidth allocation problem
• Provided algorithms to achieve provably optimal bandwidth allocation
• Demonstrated application to optical circuit provisioning