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Scalable Traffic Grooming in Optical Networks
George N. Rouskas
Department of Computer Science
North Carolina State University
Joint work with: Zeyu Liu, Hui Wang
Funded by the NSF under grant CNS-1113191
ACP 2012 – November 10, 2012 – p.1
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Outline
Motivation and Challenges
Scalable Optical Network Design
Routing and Wavelength assignment (RWA)
Traffic Grooming
Conclusion and Future Directions
ACP 2012 – November 10, 2012 – p.2
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Optical Network Design
Optical networks: the foundation of the global network infrastucture
Network design and planning crucial to operation of the Internet:
QoS, support of critical applications
survivability to failures
economics
· · ·
ACP 2012 – November 10, 2012 – p.3
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Challenges
Network design problems are hard
Optimal solutions do not scale with
network size
number of wavelengths (≈ 100/fiber currently)
ACP 2012 – November 10, 2012 – p.4
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Challenges
Network design problems are hard
Optimal solutions do not scale with
network size
number of wavelengths (≈ 100/fiber currently)
“What-if” analysis: substantial investments to explore sensitivity to:
forecast traffic demands
capital/operational cost assumptions
service price structures
· · ·
ACP 2012 – November 10, 2012 – p.4
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Traffic Grooming: Airline Analogy
ACP 2012 – November 10, 2012 – p.5
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Traffic Grooming as Optimization Problem
Inputs to the problem:
physical network topology (fiber layout)
number of wavelengths W and their capacity C
traffic matrix T = [tsd] → int multiples of unit rate (e.g., OC-3)
Output:
logical topology
traffic grooming on lightpaths
lightpath routing and wavelength assignment (RWA)
Objective:
minimize the number of lightpaths so as to carry the traffic
ACP 2012 – November 10, 2012 – p.6
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Traffic Grooming Subproblems
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ACP 2012 – November 10, 2012 – p.7
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Traffic Grooming Subproblems
1
2
3
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5
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Logical topology design → determine the lightpaths to be established
ACP 2012 – November 10, 2012 – p.7
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Traffic Grooming Subproblems
1
2
3
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Logical topology design → determine the lightpaths to be established
Traffic routing → route traffic on virtual topology
ACP 2012 – November 10, 2012 – p.7
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Traffic Grooming Subproblems
1
2
3
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Logical topology design → determine the lightpaths to be established
Traffic routing → route traffic on virtual topology
Lightpath routing → route the lightpaths over the physical topology
ACP 2012 – November 10, 2012 – p.7
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Traffic Grooming Subproblems
1
2
3
4
5
6
Logical topology design → determine the lightpaths to be established
Traffic routing → route traffic on virtual topology
Lightpath routing → route the lightpaths over the physical topology
Wavelength assignment → assign wavelengths to lightpaths w/o clash
ACP 2012 – November 10, 2012 – p.7
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Airline Analogy (2)
Lightpaths, logical topology ↔ Flights, flight routes
Traffic routing ↔ Travel itinerary
Electronic ports ↔ Gates
Grooming switch ↔ Hub airport
Wavelengths ↔ Gate timeslots
ACP 2012 – November 10, 2012 – p.8
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Traffic Grooming Complexity
. . . . . .S 1 2 3 n n+1 n+2 n+3 n+4 2n+1 D
Problem instance:
unidirectional linear (path) network
logical topology and RWA is given
traffic either bifurcated or not bifurcated
Objective: find a routing of traffic onto the lightpaths
Result: problem is NP-complete → reduction from Subset Sums
ACP 2012 – November 10, 2012 – p.9
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Challenge: Running Time
4 6 8 10 120.01
0.1
1
10
100
1000
10000
100000
360000
tLim
N
Run
ning
tim
e (s
ec)
Original
ACP 2012 – November 10, 2012 – p.10
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Challenge: Wavelength Fragmentation
1 2 3 4 5
2
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20
22
Traffic Instance
Num
ber
of w
avel
engt
hs in
sol
utio
n
DecompositionOriginal, W
a=5
Original, Wa=10
Original, Wa=30
ACP 2012 – November 10, 2012 – p.11
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Routing and Wavelength Assignment (RWA)
Fundamental control problem in optical networks
Objective: for each connection request determine a lightpath, i.e.,
a path through the network, and
a wavelength
Two variants:
1. online: lightpath requests arrive/depart dynamically
2. offline: set of lightpaths to be established simultaneously
ACP 2012 – November 10, 2012 – p.12
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Offline RWA
Input:
network topology graph G = (V,E)
traffic demand matrix T = [tsd]
Objective:
establish all lightpaths with the minimum # of λs
maximize established lightpaths for a given # of λs
Constraints:
each lightpath uses the same λ along path
lightpaths on same link assigned distinct λs
NP-hard problem (both objectives)
ACP 2012 – November 10, 2012 – p.13
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RWA Example
61
2
3 4
5
ACP 2012 – November 10, 2012 – p.14
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RWA: Symmetry
61
2
3 4
5
ACP 2012 – November 10, 2012 – p.15
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Ring RWA: Running Time
6 8 10 12 14 16 18 20 22 24<0.001
0.01
0.1
1
10
100
1000
7200tLim
Mem
N
sol t
ime
(s)
linkpathMIS
ACP 2012 – November 10, 2012 – p.16
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Ring RWA: Running Time
6 8 10 12 14 16 18 20 22 24<0.001
0.01
0.1
1
10
100
1000
7200tLim
Mem
N
sol t
ime
(s)
linkpathMISMISD−2
ACP 2012 – November 10, 2012 – p.16
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Ring RWA: Running Time
6 8 10 12 14 16 18 20 22 24<0.001
0.01
0.1
1
10
100
1000
7200tLim
Mem
N
sol t
ime
(s)
linkpathMISMISD−2MISD−4
ACP 2012 – November 10, 2012 – p.16
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Ring RWA: Running Time
6 8 10 12 14 16 18 20 22 24<0.001
0.01
0.1
1
10
100
1000
7200tLim
Mem
N
sol t
ime
(s)
linkpathMISMISD−2MISD−4MISD−8
ACP 2012 – November 10, 2012 – p.16
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Mesh RWA
Path formulation:
compact formulation for optimal symmetric solutions
fast, close to overall optimal
ACP 2012 – November 10, 2012 – p.17
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Symmetric Solution: Running Time
NSF (14) German (17) EON (20) USA (32)1
10
100
1000
10000
72000
tLim
Network Topologies (Number of Nodes)
Sol
utio
n T
ime
(S
ec)
Original ILP, Asymmetric TrafficOriginal ILP, Symmetric TrafficNew ILP, Asymmetric TrafficNew ILP, Symmetric Traffic
ACP 2012 – November 10, 2012 – p.18
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Symmetric Solution: Quality
2 4 6 8
10
20
30
40
50
60
70
Tmax
Sol
utio
n V
alue
, W
LFAP Heuristic, Asymmetric TrafficLFAP Heuristic, Symmetric TrafficOriginal ILP, Asymmetric TrafficNew ILP, Asymmetric TrafficOriginal ILP, Symmetric TrafficNew ILP, Symmetric Traffic
ACP 2012 – November 10, 2012 – p.19
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Traffic Grooming: Integrate MISD
4 6 8 10 120.01
0.1
1
10
100
1000
10000
100000
360000
tLim
N
Run
ning
tim
e (s
ec)
Originalw/ MISD
ACP 2012 – November 10, 2012 – p.20
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Traffic Grooming Decomposition
Decompose and solve the two problems sequentially:
1. Logical topology and traffic routing
2. Routing and wavelength assignment
ACP 2012 – November 10, 2012 – p.21
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Traffic Grooming Decomposition
Decompose and solve the two problems sequentially:
1. Logical topology and traffic routing→ determine lightpaths→≤ objective value of integrated problem
2. Routing and wavelength assignment
ACP 2012 – November 10, 2012 – p.21
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Traffic Grooming Decomposition
Decompose and solve the two problems sequentially:
1. Logical topology and traffic routing→ determine lightpaths→≤ objective value of integrated problem
2. Routing and wavelength assignmentroute and color lightpaths from Step 1fast (for rings and medium mesh networks)
ACP 2012 – November 10, 2012 – p.21
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Traffic Grooming Decomposition
Decompose and solve the two problems sequentially:
1. Logical topology and traffic routing→ determine lightpaths→≤ objective value of integrated problem
2. Routing and wavelength assignmentroute and color lightpaths from Step 1fast (for rings and medium mesh networks)
Optimal for instances thatare not λ-limited
1 2 3 4 5
2
4
6
8
10
12
14
16
18
20
22
Traffic Instance
Num
ber
of w
avel
engt
hs in
sol
utio
n
DecompositionOriginal, W
a=5
Original, Wa=10
Original, Wa=30
ACP 2012 – November 10, 2012 – p.21
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Logical Topology and Traffic Routing Problem
i jb integer
ij
Independent of physical topology
Integer variables are not binary→ LP relaxation possible
ACP 2012 – November 10, 2012 – p.22
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Iterative Algorithm
1. thresh ← 0
2. Relax integrality constraints on lightpath variables s.t.:bij − ⌊bij⌋ > thresh
3. Solve relaxed problem
4. If all variables integer, stop
5. If thresh > .8, stop
6. thresh + = 1/C
7. Repeat from Step 2
ACP 2012 – November 10, 2012 – p.23
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Iterative Algorithm: Quality
100
110
120
130
140
0 0.2 0.4 0.6 0.8 1
% fr
om o
ptim
al
thresh
N = 8N = 16N = 24N = 32
ACP 2012 – November 10, 2012 – p.24
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Iterative Algorithm: Running Time
0.01
0.1
1
10
100
1000
10000
50000
8 16 24 32
Run
ning
tim
e (s
ec)
N
thresh = 0.8
ACP 2012 – November 10, 2012 – p.25
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Conclusion & Ongoing Research
First steps towards efficient network design
scalable techniques on commodity hardware
lower the barrier to entry
focus on exploring design options, not ILP details
ACP 2012 – November 10, 2012 – p.26
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Conclusion & Ongoing Research
First steps towards efficient network design
scalable techniques on commodity hardware
lower the barrier to entry
focus on exploring design options, not ILP details
Many open problems:
impairment-aware RWA
shared protection, survivable grooming
routing and spectrum allocation in elastic optical networks
ACP 2012 – November 10, 2012 – p.26