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

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

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

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

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

Traffic Grooming: Airline Analogy

ACP 2012 – November 10, 2012 – p.5

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

Traffic Grooming Subproblems

1

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6

ACP 2012 – November 10, 2012 – p.7

Traffic Grooming Subproblems

1

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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

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

ACP 2012 – November 10, 2012 – p.7

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

ACP 2012 – November 10, 2012 – p.7

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

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

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

Challenge: Running Time

4 6 8 10 120.01

0.1

1

10

100

1000

10000

100000

360000

tLim

N

Run

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tim

e (s

ec)

Original

ACP 2012 – November 10, 2012 – p.10

Challenge: Wavelength Fragmentation

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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

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

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

RWA Example

61

2

3 4

5

ACP 2012 – November 10, 2012 – p.14

RWA: Symmetry

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5

ACP 2012 – November 10, 2012 – p.15

Ring RWA: Running Time

6 8 10 12 14 16 18 20 22 24<0.001

0.01

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7200tLim

Mem

N

sol t

ime

(s)

linkpathMIS

ACP 2012 – November 10, 2012 – p.16

Ring RWA: Running Time

6 8 10 12 14 16 18 20 22 24<0.001

0.01

0.1

1

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100

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7200tLim

Mem

N

sol t

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(s)

linkpathMISMISD−2

ACP 2012 – November 10, 2012 – p.16

Ring RWA: Running Time

6 8 10 12 14 16 18 20 22 24<0.001

0.01

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1

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7200tLim

Mem

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linkpathMISMISD−2MISD−4

ACP 2012 – November 10, 2012 – p.16

Ring RWA: Running Time

6 8 10 12 14 16 18 20 22 24<0.001

0.01

0.1

1

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100

1000

7200tLim

Mem

N

sol t

ime

(s)

linkpathMISMISD−2MISD−4MISD−8

ACP 2012 – November 10, 2012 – p.16

Mesh RWA

Path formulation:

compact formulation for optimal symmetric solutions

fast, close to overall optimal

ACP 2012 – November 10, 2012 – p.17

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

Symmetric Solution: Quality

2 4 6 8

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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

Traffic Grooming: Integrate MISD

4 6 8 10 120.01

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Originalw/ MISD

ACP 2012 – November 10, 2012 – p.20

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

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

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

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

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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

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

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

Iterative Algorithm: Quality

100

110

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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

Iterative Algorithm: Running Time

0.01

0.1

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50000

8 16 24 32

Run

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N

thresh = 0.8

ACP 2012 – November 10, 2012 – p.25

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

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