Impairment-AwareRouting and Wavelength

Assignment (IA-RWA)

WDM link

Generation of multiple streams of light each at a different wavelength Combination of the streams into an optical fiber (Single Mode) Amplification of the optical signals as required Separation of the multiplexed stream into its component streams Reception of the optical streams by wavelength specific receivers

Point-to-Point vs Wavelength Routed

Point-to-Point WDM Electrical Packet Switching Packet processing overhead Efficient bandwidth utilization Poor scalability, Good flexibility High energy consumption

Wavelength RoutedCircuit switching (end-to-end) No packet processing Inefficient bandwidth utilization Good scalability, Mediocre flexibility Low energy consumption

P2P WDM P2P WDM

P2P WDM

AB

C

D

P2POpaque

OXC OXC

OXC

AB

C

D

Transparent

Wavelength-Routed WDM networks Physical topology: A set of routing nodes connected by fiber links Optical Cross-connect - OXC: No O-E-O conversion Lightpath: A lightpath has to be setup before the data transmission. A

Lightpath remains in the optical domain from src to dst Logical topology: The set of src-dst pairs connected through lightpaths

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W ave l e ng th R o ute r s :

L i g htpaths :

OXC

OXC

OXC

OXC

OXCOXC

OXC

Wavelength reuseWavelength reuse

# wavelengths # wavelengths << << # OXCs# OXCs

Routing and Wavelength AssignmentRouting and Wavelength Assignment

OXC Architecture

3x3switch

3x3switch

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Optical Cross Connect

(OXC)

Protection Mechanisms

The 1+1 protection. No protocol is neededWorking fibre

Protection fibre

The 1:1 and 1:N protection. Signaling protocol is needed

1+1 is faster than 1:1 but in the latter case the sparefibre could be used for low priority traffic (extra Tx, Rx)

Wavelength Routing Pros and Cons

Setting up a lightpath is like setting up a circuit (a 2-way process

with Req and Ack): RTT = tens of ms

Pros: good for smooth traffic

Mature OXC technology (msec switching time)

QoS guarantee due to fixed BW reservation

Cons: BW inefficient for bursty (data) traffic wasted BW during off/low-traffic periods

very coarse granularity (OC-48 and above)

limited # of wavelengths (thus # of lightpaths)

RWA: Routing and Wavelength Assignment Definition

Given: network topology, end-to-end connection requests

Problem: Determine routes and wavelengths for the requests

Offline RWA (network planning phase) The entire set of requests are given in advance (traffic matrix).

Online RWA (network operation phase) Requests arrive randomly over time and are served one-by-one

Objective: Minimizing the Overall Blocking Probability

Transparent wavelength routed networks

All-optical transparent networks: advantages in capacity, cost and energy The transmission quality is affected by physical layer impairments (PLIs) Physical layer blocking: the signal detection at the receiver may be

infeasible Impairment aware (IA)-RWA algorithms

Pure RWA - problem definition

Input:

Network topology: connected graph G=(V,E)

V: set of nodes, assumed not to be equipped with wavelength converters

E: set of point-to-point single-fiber links

Each fiber is able to support a set C={1,2,…,W} of W distinct wavelengths

A-priori known traffic scenario given in a matrix of nonnegative integers Λ

Output:

the RWA instance solution, in the form of routes and assigned wavelengths

the number of wavelengths required to route all the connections

Objective: minimize the number of used wavelengths

LP Formulation and Flow Cost FunctionFlow Cost Function

1

ll l

l

wF f w

W w

Increasing and Convex (to imply a greater amount of ‘undesirability’ when a link becomes congested)

Approximated by a piecewise linear function Integer break points (makes Simplex yield

integer optimal solutions with high probability)

Parameters: s,d V: network nodes wC: an available wavelength l E: a network link pPsd: a candidate path

Constant: Λsd: the number of requested connections from node s to d

Variables: xpw: an indicator variable, equal to 1 if path p occupies wavelength w, else 0 Fl: the flow cost function value of link l

RWA LP FORMULATION

minimize : ll

F

subject to the following constraints:

Distinct wavelength assignment constraints,

|

1,pwp l p

x

for all lE, for all wC

Incoming traffic constraints,

sd

pw sdp P w

x

, for all (s,d) pairs

Flow cost function constraints,

|

l l pwp l p w

F f w f x

The integrality constraint is relaxed to 0 1.p wx

We obtain integer solutions in 98% of the problem instances!

Random perturbation

In the general multicommodity problem, a flow

that is served by more than one paths has equal

sum of first derivates over the links of those

paths and also these paths are of equal length

In our problem a request that is served by more

than one lightpaths has equal sums of first

derivates over the links of these paths

To avoid such cases, we multiply the slopes of

each variable on each link with a random

number that is close to 1

In this way, the cases that two variables have

equal derivates over the links that comprise a

path are reduced, and thus we obtain more

integer solutions

X2=0.5X1=0.5

Handling non-integer solutions

Make Simplex yield integer optimal solutions

Piecewise linear cost functions

Random perturbation technique

Still the solution may be non-integer

Iterative fixings Fix the integer variables of the solutions and solve the remaining (reduced) LP problem

The objective cost does not change if we get to an integer solution it is optimal

When fixing does not further increase the integrality, we proceed to the rounding process

Iterative rounding Round a single variable, the one closest to 1, and continue solving the reduced LP problem

Rounding helps us move to a higher objective and search for an integer solution there

If the objective changes we are not sure anymore that we will find an optimal solution

Pure RWA algorithm

Use a pure RWA algorithm that is based on a LP-relaxation formulation

The algorithm consists of 4 steps

1. We calculate a set of candidate paths

2. Using the set of candidate paths we

formulate the RWA instance as a LP problem

and use Simplex to solve it

3. We handle a fractional (non-integer) solution,

by applying iterative fixing and rounding

methods

4. We handle non infeasible instances (when

the RWA instance cannot be served with the

given number of wavelengths)

Traffic Matrix ΛNetwork Topology G=(V,E)

Number of Available Wavelengths W

k

RWA formulationLP relaxation

RoundingRound a fractional variable to 1 and re-execute Simplex

SolutionRouted lightpaths, blocking

Integer Solution?

Simplex

yes

no

Candidate paths

Calculate the k-shortest paths for all connections (s,d) for which Λsd≠0

Feasible?yes no

Integrality is not further increased

Increase the number of available wavelengths and go to Phase 2Once the solution has been found we remove the additional

wavelengths (blocking >0)

Phase 1

Phase 2

Phase 3

Phase 4

FixingFix the integer variables up-to now and re-execute Simplex

IA-RWA problem

IA-RWA objective: minimize the number of wavelengths used (network layer)

and also select lightpaths with acceptable transmission quality (physical layer)

For IA-RWA algorithms we classify physical layer impairments (PLIs) into:

1st class PLIs: generated by the same lightpath (ASE, CD, PMD, FC, SPM)

2nd class PLIs: generated due to inter-lightpath interference (XT, XPM, FWM)

PLIs of the 2nd class make routing decisions for one lightpath affect and be

affected by decisions made for the other lightpaths

Solution:

1. Worst case interference assumption

2. Actual interference: cross layer optimization

Worst Case and Actual Interference

Worst case interference algo: Consider PLIs that do not depend on interference (1st class PLIs)

Assume all wavelengths active (2nd class PLIs)

Prune candidate lightpaths that do not have acceptable QoT

Hamburg

Berlin Hannover

Bremen

Essen

Köln

Düsseldorf

Frankfurt

Nürnberg

Stuttgart Ulm

München

Leipzig

Dortmund

Actual interference: cross layer optimization algo: Consider PLIs that do not depend on interference (1st class PLIs)

Prune candidate lightpaths that do not have acceptable QoT

Formulate the interference among lightpaths into the RWA

Illustrative example: DTnet topology - single connection request between all (s,d) pairs

The reduction in the solution space can deteriorate wavelength performance

Physical layer evaluation: Q-factor

Use the Q factor to estimate the feasibility of a lightpath The Q factor is related to the BER Analytical formulas can be used to calculate the Q factor

I’1’

I’0’ σ’0’

σ’1’

Proposed IA-RWA algorithms

Indirect IA-RWA algo: Constrain the impairment generating sources

1. the length and the number of hops of a path

2. the number of adjacent (and second adjacent) channels over all links of the

lightpath

3. the number of intra-channel generating sources (lightpaths crossing the same

switch utilizing the same wavelength) along the lightpath

Direct IA-RWA algo: Use the definition of Q factor and noise variance

related parameters to define physical layer constraints into the RWA

(Soft) constrain the number of adjacent channel interfering sources on lightpath (p,w)

B is a large constant used to activate/deactivate the constraint

Similarly we constrain the second-adjacent channel interfering sources

Indirect (Parametric) IA-RWA algo

Number of active adjacent channels(Affected PLIs: Intra-XT, XPM and FWM)

(Soft) constrain the number of intra-XT interfering sources on lightpath (p,w)

B is a large constant used to activate/deactivate the constraint

Similarly we constrain the second-adjacent channel interfering sources

Number of intra-channel XT sources

n0 n1 n2 n3 n4

w w

ww w ww

pp’ p’’’

l1 l2 l3 l4

p’3

ending here

p’’

wsource

pdestination

w

Curry the surplus variables in the minimization objective

Direct (Sigma Bound) IA-RWA algo

For each candidate lightpath (p,w) inserted in the RWA formulation, we calculate an

upper bound on the interference noise variance it can tolerate, after accounting for

the impairments that do not depend on the utilization of the other lightpaths (account

for 1st Class PLIs).

Then using noise-variance related parameters per link we can constrain the

interference (due to 2nd Class PLIs) accumulated on lightpath (p,w)

If the selected lightpaths satisfy these constraints they have, by definition,

acceptable quality of transmission

XPM from adjacent channels XPM from second adjacent channelsintra-XT

2 2 2, ', , ', 1 ', 1 2 , ', 2 ', 2

{ '| '} { '| '} { '| '}XT n p w XPM l p w p w XPM l p w p w

p n p p l p p l p

s x s x x s x x

2max

{ | endof }

( , )pw pl p n l

B x S p w B

2 2 2,'1' ,'1' max( , ) ( , ) ( , )XT XPMp w p w p w

Performance evaluation results

Simulation platform

Matlab + LINDO API

Generic DT network topology

Traffic Scenarios

Random traffic matrix generator

DTnet actual traffic matrix

Physical Layer Evaluation: Q-Tool

Developed within DICONET project

Uses analytical models to calculate the

Q factor of lightpaths

Realistic physical layer parameters

Hamburg

Berlin Hannover

Bremen

Essen

Köln

Düsseldorf

Frankfurt

Nürnberg

Stuttgart Ulm

München

Leipzig

Dortmund

Node

SMFPre-DCM DCF

Node

Post-DCMSMF

N-1 spans

N-th SMF span

NodeNode

SMFSMFPre-DCMPre-DCM DCFDCF

NodeNode

Post-DCMSMFSMF

N-1 spans

N-th SMF span

Pure RWA performance

100 RWA instances ILP min-max: optimality criterion LP min-max: running time & integrality criteria The proposed LP-relaxation+piecewise linear

costs has superior performance The performance is Improved with the

random perturbation technique

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61 6359

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Inst

ance

s su

re t

o ob

tain

opt

imal

sol

utio

nILP

LP-min_max

LP-piecewise

LP-piecewise+random_perturbation

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92

840

3992

1723

3038

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31

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31

1.E+00

1.E+01

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1.E+04

0.5 1 1.5 2Load

Ave

rage

run

ning

tim

e (s

ec)

ILP-min_max

LP-min_max

LP-piecewise

LP-piecewise+random perturbation

7.70

14.01

20.53

26.66

7.76

14.03

20.56

26.68

7.72

14.02

20.54

26.68

7.70

14.02

20.53

26.66

0

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30

0.5 1 1.5 2Load

Ave

rag

e n

um

be

r o

f w

ave

len

gth

s

ILP-min_max

LP-min_max

LP-piecewise

LP-piecewise+random perturbation

Indirect and Direct IA-RWA 100 RWA instances

W=16 available wavelengths

Algorithms: Pure RWA

Indirect P-IA-RWA

Direct SB-IA-RWA

The proposed IA-RWA algorithms reduce

the (physical layer) blocking

Additional wavelengths are required to

spread the lightpaths and avoid interference

The direct SB-IA-RWA algo can find zero

blocking solutions

The direct SB-IA-RWA algo maintains zero

blocking up to ρ=0.8, after which the 16

available wavelengths are not enough

0

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10

0.5 0.6 0.7 0.8 0.9 1

Load

Blo

ckin

g ra

tio

pure RWA

P-IA-RWA

SB-IA-RWA

Load ρ=0.5

Algorithm Average

number of wavelengths

% of optimal

solutions

Average number of fixing and

roundings

Average running

time Pure RWA 7.70 0.94 1 1.27

P-IA-RWA 10.32 0 23 112.2

SB-IA-RWA 9.27 0 15 20.4

Direct IA-RWA algo performance

Direct SB-IA-RWA algorithm, solved using

The proposed LP-relaxation technique

ILP

100 random RWA instances

Find zero blocking solutions

Using ILP we were able to solve all

instances within 5 hours up to ρ=0.7 load

Using the LP-relaxation the optimality is

lost in 2 or 3 instances but the execution

time is maintained very low

9.27

10.78

12.72

14.17

15.32

16.28

9.24

10.76

12.70

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Wa

vele

ng

ths

to a

chie

ve z

ero

blo

ckin

g

LP SB-IA-RWA

ILP SB-IA-RWA

20.4

47.662.4

101

179252

40.7

227.4

2550

1.E+01

1.E+02

1.E+03

1.E+04

0.5 0.6 0.7 0.8 0.9 1Load

Ave

rage

run

ning

tim

e (s

ec)

LP SB-IA-RWA

ILP SB-IA-RWA

Realistic traffic matrix

Realistic traffic matrix

(381 connections load ρ=2.05)

The propose IA-RWA algorithms

reduce the physical layer blocking

The direct SB-IA-RWA finds zero

blocking solution

with W=36

Running time: 20 minutes

acceptable for the realistic network

and traffic load

0

3

6

9

12

15

30 32 34 36 38 40Number of available wavelengths

Blo

ckin

g ra

tio

pure RWA

P-IA-RWA

SB-IA-RWA

Dynamic ΙΑ-RWA Algorithm

Input:

New connection request

Current network state

Objective: serve the connections and minimize blocking

over (infinite) time

We use a multicost algorithm with 2 phases

1. Calculate the set of non-dominated paths from the given

source to the given destination

2. Choose the lightpath that minimizes the objective function

(offline algos) (online algos)(offline algos) (online algos)

Calculating the Set of Non-Dominated Paths

Cost vector of link l:

Vector maps the utilization of wavelengths

The cost vector of path p can be calculated based on the cost vectors

of links l=1,2,...,m, that comprise it

The cost parameters of a path can be combined so as to calculate the

Q factors of the available lightpaths over that path

Prune the solution space

For each p, we check the Q factor of available lightpaths and we make

unavailable those that do not have acceptable performance

Stop extending the paths that do not have σταματάμε να επεκτείνουμε

μονοπάτια αν δεν έχουν τουλάχιστον ένα διαθέσιμο μήκος κύματος

Vl = (dl, lG , 2'1',l , 2

'0',l ,lW )

22 10100,1,

11

2 2

'1', '0 ',1 11 1 1

221010 (1,..., ),, , ,,( , , , ,* ) , &

mm

lli li l

iim mm m m

lll ll ll l l

GG

lp l l lWd mGV d G W p

lW

Q Qmax

dmin

Calculating the Set of Non-Dominated Paths

Domination relationship between two paths

p1 dominates p2 (p1 > p2) iff

Using the above definitions we use a multicost algorithm, which is a

generalization of Dijkstra algorithm, to compute the set of non-dominated

paths Pn-d from the given source to the given destination

By definition, the paths that are included in Pn-d have

At least one available wavelength

The available wavelength have acceptable transmission performance

(Q factor)

Q QQmax

dmin

1 2 1 2 1 2 and andp p p p p pd d W W Q Q

Optimization Policies

i) Most Used Wavelength (MUW)

We order the lightpaths to decreasing

wavelength utilization order and select the

one that is used more in the network.

ii) Better Q performance (bQ)

We select the lightpath with the higher Q

factor value

iii) Mixed better Q and most used wavelength

(bQ-MUW)

From the set of available lightpaths we select

those with Q values no less than 0.5dB than

the highest Q value and then apply the MUW

policy to this new set of lightpaths

1.E-03

1.E-02

1.E-01

1.E+00

100 120 140 160 180 200load (erlangs)

Blo

ckin

g pr

obab

ility

(%

)

MUW

bQ

bQ-MUW

`

1.E-03

1.E-02

1.E-01

1.E+00

100 120 140 160 180 200load (erlangs)

Ave

rage

num

ber

of r

erou

tings

MUW

bQ

bQ-MUW

`

We evaluated 3 optimization policies (that correspond to 3 different IA-RWA algorithms)

The “whole” picture

Continuing work to build the NPOT….

Network Planning and Operation Tool

(NPOT)

Network Planning and Operation Tool

(NPOT)

Planning ModePlanning Mode Operation ModeOperation Mode

Distributed Integration Scheme

Distributed Integration Scheme

Centralized Integration Scheme

Centralized Integration Scheme

•Offline IA-RWA

•Regenerator Placement

•Monitor Placement

•Offline IA-RWA

•Regenerator Placement

•Monitor Placement

•Online IA-RWA

•Failure localization

•QoT Degradation

•Online IA-RWA

•Failure localization

•QoT Degradation

•Online IA-RWA

•Failure localization

•QoT Degradation

•Online IA-RWA

•Failure localization

•QoT Degradation

Control