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Optical Communications
WDM technologies
Point to Point WDM
Wavelength Routed WDM
RWA and IA-RWA
Optical Communications
WDM technologies
Point to Point WDM
Wavelength Routed WDM
RWA and IA-RWA
Input data Output data
Transmitter receiver
Η ηερλνινγία πεξηγξάθεηαη από ην ξπζκό κεηάδνζεο Β (bps) θαη ηελ
απόζηαζε L (km) γηα δεδνκελν BER.
Σήκα ηωλ B bps αλαθηάηαη ζηα L km κε BER ηωλ 10-9.
Έζηω όηη θάπνηνο ζέιεη λα κεηαδώζεη ΒΤ bps ζε LT km.
Τόηε ρξεηάδνληαη επαλαιεπηέο θαη ζύλδεζκνη.
Οηθνλνκηθά, ν ζεκαληηθόο αξηζκόο είλαη ην γηλόκελν ΒxL (bps x km).
Οπηηθέο επηθνηλωλίεο
Energy is confined to the core of the fiber by giving the core a slightly higher
refractive index. The sheath protects the core and keeps moisture out.
Fiber link
Α (dB/km) = ζηαζεξά εμαζζέλεζεο ηεο ίλαο (εμαξηάηαη από ην κήθνο θύκαηνο λ θαη
ηελ πνηόηεηα ηεο ίλαο)
Fiber Attenuation
Huge bandwidth: 2nd window usable spectrum=15THz.
More than 30 Tbps can be used
Phone call 64Kbps 460 million calls !!
Low attenuation:
0.2 Db/km compared to 30 dB/km for
coaxial cable
• L = απόζηαζε πνκπνύ/δέθηε
• PT = εθπεκπόκελε ηζρύο = 1mW
• PR = 10-4.5 mW = απαηηνύκελε δερόκελε ηζρύο ζηα Β=1Gbps γηα BER= 10-9
• Α = 0.2 dB/Km = ζηαζεξά εμαζζέλεζεο
κέγηζην κήθνο:
ζύγθξηζε κε νκναμνληθό θαιώδην:
ζεκείωζε 1: ην L απμάλεηαη γξακκηθά κε ην 1/Α θαη ινγαξηζκηθά κε ην ΡΤ ή ην ΡR
ζεκείωζε 2:
γηα νκναμνληθό θαιώδην
Γηα BER=10-9 θαη InGaAs PIN δέθηε
γηα νπηηθή ίλα
ζεκείωζε 2:
Example
ίνα
πομπός δέκτης
έξοδος
σ = αL (ην α εμαξηάηαη απ’ ηελ ίλα)
Έζηω όηη ε είζνδνο είλαη 101010 κε ξπζκό bit B=1/T.
έξοδοςείσοδος
Γηα λα κελ νδεγήζεη ε δηαπιάηπλζε ζε ζθάικαηα,
απαηηείηαη, πρ, :
άλω θξάγκα ζην γηλόκελν ΒxL ιόγω ηεο δηαπιάηπλζεο.
Σν όξην δηαπιάηπλζεο καδί κε ην όξην εμαζζέλεζεο θαζνξίδεη ην κέγηζην ρξεζηκνπνηήζηκν
κήθνο κηαο ίλαο γηα δεδνκέλν Β.
είσοδος
Dispersion
Δηαθνξεηηθέο γωλίεο δηάδνζεο θαινύληαη ηξόπνη δηάδνζεο.
Δηαθνξεηηθνί ηξόπνη δηάδνζεο ηαμηδεύνπλ δηαθνξεηηθέο απνζηάζεηο γηα λα
πεξάζνπλ ηα L km ηεο ίλαο.
Η απόζηαζε πνπ ηαμηδεύεηαη από ηνλ ηξόπν δηάδνζεο γωλίαο θ ηζνύηαη κε
L/cosθ, ν ρξόλνο είλαη .
Η δηαθνξά ρξόλνπ αλάκεζα ζηελ ηαρύηεξε θαη ηελ αξγόηεξε ζπληζηώζα, πνπ
δίλεη θαη ην άλω θξάγκα ηνπ γηλνκέλνπ BxL, όπωο απηό ηίζεηαη από ην ηξνπηθό
ζθόξπηζκα, ηζνύηαη κε
Τξνπηθή Γηαζπνξά (modal dispersion)
• Σν όξην ηξνπηθήο δηαζπνξάο δίλεη
• Δξακαηηθή βειηίωζε κε graded index (GRIN) ίλεο
κπνξεί λα επηηεπρζεί
• ε κνλνηξνπηθέο ίλεο ε ηξνπηθε δηαζπνξα απνπζηάδεη, αθνύ ε δηάκεηξνο ηνπ ππξήλα
είλαη πεξίπνπ 8κm.
Παξνπζηάδεηαη, ωζηόζν, δηαζπνξα ‘πιηθνύ’, θαζώο ν δηαζιαζηηθόο δείθηεο είλαη κε
γξακκηθή ζπλάξηεζε ηνπ λ. Αλαιπηηθά πξνθύπηεη .
Τξνπηθή Γηαζπνξά (II)
Μεραληζκόο θωηόο: αλαζπλδπαζκόο ηωλ ειεθηξνλίωλ ζηε δώλε αγωγηκόηεηαο κε νπέο ζηε
δώλε ζζέλνπο : ε πεξίζζεηα ελέξγεηα εθπέκπεηαη ωο θωηόλην.
Οπηηθή ηζρύο (mW):
Σππηθή ηζρύο = 1mW , Φαζκαηηθό εύξνο ≈ 100nm
Σν θωο πνπ εθπέκπεηαη είλαη αζπλεπέο (incoherent) δειαδε πεξηέρεη δηαθνξεηηθέο
ζπρλόηεηεο/θάζεηο.
ζώνη αγωγιμότητας
ζώνη σθένους
ενδιάμεση ενέργεια
Sources (e.g. LEDs)
Αληρλεπηέο
Έλαο αληρλεπηήο πξέπεη λα θαζνξίζεη αλ ζηαιζεθε ‘0’ ή ‘1’.
Ο αληρλεπηήο θωηνλίωλ (photo detector) ζπλήζωο είλαη κηα ΡΙΝ δίνδνο.
ενισχυτής κύκλωμα απόφασηςεξισωτής
Σν πιηθό ηεο ΡΙΝ δηόδνπ είλαη ίδην κε απηό ηωλ LED θαη LD.
Σν ξεύκα είλαη αλάινγν κε ηνλ αξηζκό ηωλ θωηνλίωλ πνπ αθίθλπληαη.
Η επαηζζεζία κεηώλεηαη κέζω ηνπ ‘shot’ ζνξύβνπ.
εσωτερικός ημιαγωγός
Το φωτόνιο διεγείρει ένα ζεύγος οπή-ηλεκτρόνιο αν
η ενέργειά του είναι μεγαλύτερη από Wg.
Το ηλεκτρόνιο ρέει στο ηλεκτρικό κύκλωμα.
Why Optical Communications?
Huge bandwidth (more than 30 Tbps)
Low signal attenuation (as low as 0.2 dB/km)
Low signal distortion
Low power consumption Low power requirement
Low material usage Small space requirement
Low Cost
Need: Appropriate multiplexing techniques and network architecture
Optical Multiplexing TechniquesWavelength or Frequency = Wavelength Division Multiplexing (WDM)
Time slots = Time Division Multiplexing (TDM)
Wave shape or spread spectrum = Code Division Multiplexing (CDM)
Optical TDM
Each end-user should be able to synchronize to within one time slot
Optical TDM bit rate = aggregate rate over all TDM channels
Optical CDM: optical SCM chip rate may be much higher than each
user‟s data rate
WDM: current favorite
- Αλεμαξηήηωο δηακνξθωκέλα θαλάιηα.
- Κάζε θαλάιη ιεηηνπξγεί ζηε κέγηζηε ειεθηξνληθή ηαρύηεηα (1-10-40 Gb/s).
To WDM είλαη ζαλ ηνλ FDM γηα νπηηθά δίθηπα.
Τν CDMA είλαη θπξίωο γηα αζύξκαηα δίθηπα
Έλαο κεηαγωγέαο
θπθιώκαηνο
αλα-αλαζέηεη
είηε ζπρλόηεηεο είηε
slots ζηηο
εμόδνπο
WDM and DWDM
Wavelength Division Multiplexing (WDM) uses multiple
wavelengths to transmit information over a single fiber
Each wavelength is a separate channel
Dense WDM (DWDM) is the extension of WDM which
allows simultaneous transmission of 16+ wavelengths
Physical layer (OSI-layer 1)
Extremely high capacity and efficient optical transport
Prevalent in Long Haul applications, but now emerging in
Metropolitan Area Networks (MAN)
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
From WDM to DWDM
Early systems used 2 wavelengths which were widely spaced
WDM came to be called DWDM with advent of 16+ channels More channels on a wavelenght band with finer grain divisions (< 200 GHz)
More channels and faster line rates = more optical capacity 1.6 Tbps capacity in commercially available, i.e. 160 ιs @ 10 Gigabits/s each
Fiber Attenuation
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 Routed
Circuit 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
P2P
Opaque
OXC OXC
OXC
A
B
C
D
Transparent
Παπάδειγμα : αναβάθμιζη ηηρ συπηηικόηηηαρ μεηάδοζηρ ενόρ ζημείο-ππορ-ζημείο
ζςνδέζμος μεηάδοζηρ από OC-48 (2.5 Gbps) ζε OC-192 (10 Gbps) μέζυ ηπιών δςναηών
λύζευν (‘OC’ = ‘οπηικό κανάλι’, ‘OC-n’ = πςθμόρ δεδομένυν n x 51.84 Mbps πεπίπος):
1. Λύζη ‘πολλαπλών ινών’ – εγκαηάζηαζη επιππόζθεηυν ινών και ηεπμαηικού εξοπλιζμού
2. Λύζη WDM 4 καναλιών (δερ ζσήμα)
3. Λύζη ‘ςτηλόηεπηρ ηλεκηπονικήρ ηασύηηηαρ’ – OC-192
ηεξκαηηθό πνιππιεμίαο ηεξκαηηθό απνπνιππιεμίαο
ηεξκαηηθόο εμνπιηζκόο
WDM πνκπόο
(απν)πνιππιέθηεο κήθνπο θύκαηνο
νπηηθόο εληζρπηήο
νπηηθά ζπζηαηηθά:
εληζρπηήο νπηηθήο
γξακκήο
εκείν-πξνο-ζεκείν WDM ζύζηεκα εθπνκπήο 4 θαλαιηώλ
Εμέιημε ηνπ WDM δηθηύνπ: 3α. Crossconnect – Παζεηηθό Αζηέξη
Εμέιημε ηνπ WDM δηθηύνπ: 3β. Crossconnect – Παζεηηθόο Δξνκνινγεηήο
έλα NxN αζηέξη κπνξεί λα δξνκνινγήζεη N ηαπηόρξνλεο ζπλδέζεηο, όιεο ζε ιεηηνπξγία εθπνκπήο (broadcast)
- έλαο NxN δξνκνινγεηήο κπνξεί λα δξνκνινγήζεη Ν^2 ηαπηόρξνλεο ζπλδέζεηο
- πίλαθαο ζηαζεξώλ δξνκνινγήζεωλ (όρη broadcast)
OXC Architecture
3x3
switch
3x3
switch
1
1
1
1
1
1
2
2
22
2
2
1 2
1 2
1 2
1 2
1 2
1 2
Optical Cross Connect
(OXC)
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
1
2
3
4
6
5
7
1
2
3
Wavelength Routers:
Lightpaths:
OXC
OXC
OXC
OXC
OXCOXC
OXC
Wavelength reuse
# wavelengths << # OXCs
Routing and Wavelength Assignment
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 spare
fibre 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)
Optical Communications
WDM
Point to Point WDM
Wavelength Routed WDM
RWA and IA-RWA
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
ONDM 2011, Bologna, Italy 33
Integer Linear Programming (ILP) Integer variables x
minimize cT . x
subject to A . x ≤ b, x = (x1,...,xn) ∈ Ζn
The general ILP problem is NP-complete
Algorithms
Solve small-medium size ILP problems
Branch-and-bound
Cutting plane
Mixed Integer Linear Programming (MILP): integer and float variables
cost
3x1 + 2x2
ONDM 2011, Bologna, Italy 34
Convex Hall The same set of integer solutions can be described by different sets of constraints
(the same set of integer solutions can be included in different-shaped n-dimensional polyhedrons)
The convex hull is the minimum convex set that includes all the integer solutions of the problem
Given the convex hull we can use a LP algorithm to obtain the optimal ILP solution in polynomial time
The transformation of a general n-dimension polyhedron to the corresponding convex hull is difficult
(process used in cutting plane techniques)
Good ILP formulation: the feasible region defined by the linear constraints is close (tight) to the
corresponding convex hull
A large number of vertices consist of integer variables. This increases the probability of obtaining
an integer solution when solving the corresponding LP-relaxation of the initial ILP problem
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
w C: an available wavelength
l E: a network link
p Psd: 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 : l
l
F
subject to the following constraints:
Distinct wavelength assignment constraints,
|
1,pw
p l p
x for all l E, for all w C
Incoming traffic constraints,
sd
pw sd
p P w
x , for all (s,d) pairs
Flow cost function constraints,
|
l l pw
p 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
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 Λ
Network Topology G=(V,E)
Number of Available Wavelengths W
k
RWA formulation
LP relaxation
Rounding
Round a fractional variable to 1 and re-execute Simplex
Solution
Routed 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 2
Once the solution has been found we remove the additional
wavelengths (blocking >0)
Phase 1
Phase 2
Phase 3
Phase 4
Fixing
Fix 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’
Power Budget and Eye
impairments
Power budget,SPM/CD,PMD,FC
Noise and noise-like impairments
ASE, XT, XPM, FWM
XT, XPM, FWM, depend on the utilization of
the other lightpaths
σ2'1',p(w)=σ2
ASE,’1',p(w)+σ2XT,’1',p(w)+
σ2XPM,’1',p(w)+σ2
FWM,’1',p(w)
σ2'0',p(w)=σ2
ASE,’0',p(w)+σ2XT,’0',p(w)+
σ2FWM,’0',p(w)
'1', '0 ',
'1', '0 ',
( )( )
( ) ( )
p p
p
p p
I w IQ w
w w
Depend mainly on the selected lightpath,
not on the utilization of other lightpaths
I’1',p(w) I'0',p=0
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
n0 n1 n2 n3 n4w
l1
www
w+1 w+1
w-1
p'
l2 l3 l4
p''
w+1
w-1
p w
source
p w
destination
w-1
', 1 ', 1
{ '| '}
( ) adj acceptablep w p w pw p
l p p l p
Nx x B x S B
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 w
w
pp’ p’’’
l1 l2 l3 l4
p’3
ending here
p’’
w
source
pdestination
w
',
{ } { '| '}
XT acceptablep w pw p
n p p n p
Nx B x S B
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 2
max
{ | endof }
( , )pw p
l 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
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
100 100 100 100
6163
59
46
87 88 87 88
100 98 98 98
0
20
40
60
80
100
0.5 1 1.5 2Load
Insta
nces s
ure
to o
bta
in o
ptim
al solu
tion
ILP
LP-min_max
LP-piecewise
LP-piecewise+random_perturbation
7
92
840
3992
1723
3038
2
8
17
31
1
5
15
31
1.E+00
1.E+01
1.E+02
1.E+03
1.E+04
0.5 1 1.5 2Load
Avera
ge r
unnin
g t
ime (
sec)
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
5
10
15
20
25
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
2
4
6
8
10
0.5 0.6 0.7 0.8 0.9 1
Load
Blo
ckin
g r
atio
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
9
11
13
15
17
0.5 0.6 0.7 0.8 0.9 1
Load
Wa
ve
len
gth
s t
o a
ch
ieve
ze
ro b
lockin
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
Avera
ge r
unnin
g tim
e (
sec)
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 40
Number of available wavelengthsB
lockin
g r
atio
pure RWA
P-IA-RWA
SB-IA-RWA
Dynamic ΘΑ-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
Σηακαηάκε λα επεθηείλνπκε κνλνπάηηα αλ δελ έρνπλ ηνπιάρηζηνλ έλα
δηαζέζηκν κήθνο θύκαηνο
Vl = (dl, lG , 2
'1',l, 2
'0 ',l,
lW )
22 1010
0,1,
11
2 2
'1', '0 ',1 11 1 1
22
1010 (1,..., ),, , ,,( , , , ,* ) , &mm
ll
i li l
iim mm m m
lll ll ll l l
GG
lp l l lWd mGV d G W p
lW
Q Qmax
dmi
n
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
dmi
n
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 p
robabili
ty (
%)
MUW
bQ
bQ-MUW
`
1.E-03
1.E-02
1.E-01
1.E+00
100 120 140 160 180 200load (erlangs)
Avera
ge n
um
ber
of
rero
utings
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….
Control
Network Planning and
Operation Tool
(NPOT)
Network Planning and
Operation Tool
(NPOT)
Planning ModePlanning Mode Operation ModeOperation Mode
Distributed
Integration Scheme
Distributed
Integration SchemeCentralized
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
