3D-4 Internet Traffic Engineering by Optimizing OSPF Weights
An Alternative Genetic Algorithm to Optimize OSPF Weights
Transcript of An Alternative Genetic Algorithm to Optimize OSPF Weights
Communication Networks E. Mulyana, U. Killat
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An Alternative Genetic Algorithm to
Optimize OSPF Weights
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Introduction
• OSPF (IGP) use administrative metric
– Not adapt on the traffic situation
Unbalanced load distribution
• Mechanism to increase network utilization and
avoid congestion
– Changing the link weights for a given demand
– The problem is NP-hard
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OSPF Routing Problem (1)
• Each link has a cost/weight [1 ... 65535]
• Routers compute paths with Dijkstra‘s
algorithm
• ECMP even-splitting
• Given a demand and a set of weights
Load distribution (does not depend on link
capacities)
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OSPF Routing Problem (2)
Find a set
of weights
with minimal
cost
Dijkstra ,
ECMP
Objective (cost)
Function
Network topology
and link capacities
Predicted traffic
demand
Set of weights
Cost value
Utilization (max, av)
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Objective Functions
• Objective Function 1 : Stähle, Köhler, Kohlhaas
maximum & average utilization
• Objective Function 2 : Minimizing changes
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General Routing Problem
• Lower bound for shortest path (SP) routing
• No SP constraints, no splitting constraints
• LP formulation:
Objective Function
Flow Conservation
Utilization Upper Bound (t)
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The Proposed GA
The big picture The population dynamic
Start
Population
Exit
Condition Selection
Reproduction
Mutation
Add new
Population
Selection
Reproduction
Mutation
Population
50 chromosomes
Selection (parents)
8 chromosomes
Selection
(remove 10%)
Population
45 chromosomes
Offsprings
16 chromosomes
Population
61 chromosomes
Selection
(best 50 chromosomes)
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Forming a new generation
• Reproduction
– Crossover
– Arbitrary Mutation
• „Targeted“ Mutation
AV C1 C2 C3 C4
P1 P2
O4 O1
Reproduction
„Targeted“
Mutation
O3 O2
„Targeted“
Mutation
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Reproduction
5 5 6 5 7
1 2 3 3 4 Parent 1
Parent 2
Offspring 1
Offspring 2
Random 0.81
const 2
const 1 0.03
0.53
0.59
5
1
0.02
1
8
0.09
6
3
0.35
5
3 7
4
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„Targeted“ Mutation
0.4 1.4 0.1 0.8 0.3 0.6
0.1 0.6 0.7 1.2 0.4 0.6
5
1 6 5
7
1
8 3 3
4
Offspring 1
Offspring 2
Util. O1
Util. O2
Average
Average
Av - 0.2 Av + 0.2
Utilization
5
1 6 5
7
1
8 3 3
4
3
5 4
7
3
Offspring 3
Offspring 4
0.1
1.4 0.1
1.2
0.3
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Results (1)
Result of 6 routers network
6 routers
network
10 routers
network
MIP GA
Max. 35.7%
Av. 22.7%
95% match
(100 runs, 100 iterations)
Max. 96.7%
Av. 82.9%
32% match
(100 runs, 300 iterations)
• Objective function (1)
• at = 10
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Results (2)
• Objective function (2)
• at = ay = 10
Original
(reference) GA
Max. 42.9%
Av. 22.4%
Max. 35.7%
Av. 22.7%
4 link changes :
(2,1) (3,4) (4,5) (5,6)
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A Test Network
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Results (3)
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Results (4)
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Conclusion
• Alternative genetic algorithm to OSPF
routing problem, with a mutation heuristic
• Objective function (O.F.) from Stähle,
Köhler, Kohlhaas
• Enhancing this O.F. to minimize weight
changes
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Thank You !
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Convergence
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Increasing Traffic