Impairment Aware RWA Algorithms Based on Prediction Concepts - Workshop Vilanova July 7
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UNIVERSITAT POLITÈCNICA DE CATALUNYA
Impairment Aware RWA
Algorithms Based on Prediction
Concepts
Eva Marín-Tordera
Advanced Network Architectures Lab (CRAAX)
Universitat Politècnica de Catalunya, UPC
Vilanova i la Geltrú, Spain
Workshop Vilanova July 7COMPUTER ARCHITECTURE DEPARTMENT Index
• Introduction
• Part 1
– Prediction-Based Routing (PBR)
– Maximum Transmission Distance (MTD)
– MINCOD routing algorithm
– PR-MTD RWA algorithm
– PR-MTD performance evaluation
• Part 2
– Q Personik’s factor
– Planning Phase – Deterministic Algorithm
– Analysis Phase – PR-Q RWA algorithm
– Simulation framework
– PR-Q performance evaluation
• Conclusions
2
Workshop Vilanova July 7COMPUTER ARCHITECTURE DEPARTMENT Introduction
• Our research group have been actively working in the
design of RWA algorithms that utilize inaccurate network
state information.
• This experience has been transferred to the area of IA-
RWA.
1. Taking into account the Physical Impairments (PI) as a
constraint more.
2. Taking into account that the Physical information can be
inaccurate
• Work done in collaboration with: Telecom Italia,
Corecom, Politecnito di Milano and Pirelli Labs
3
Workshop Vilanova July 7COMPUTER ARCHITECTURE DEPARTMENT Part 1
• Proposal of IA-RWA algorithm PR-MTD taking
into account:
– Maximum Transmission (MTD) Distance as physical
impairment.
– Inaccurate Network state information (wavelength
availability).
– Removing update messages with wavelength
availability changes.
• Work developed in Nobel 2 Project
• Publications: ICTON 2007 and ECOC 2007
4
Workshop Vilanova July 7COMPUTER ARCHITECTURE DEPARTMENT Prediction Based Routing
Prediction Based Routing (PBR)
• Prediction → Read 2-bit counter.
• Motivation: The network state information • Update two-bit counter
(wavelength availability, regenerator usage and PI → By increasing if connection is
values) used by usual routing algorithms might not
be completely accurate. blocked.
– Assuming source routing, source nodes need → By decreasing if connection is
update messages with information about wavelength
availability, changes in PLI values and regeneration established.
capabilities in nodes. PTsource-destination
• Goal: Proposing a routing mechanism based Lightpath 2 bit counter Prediction
on prediction issues named Prediction-Based i 2 bit counter
00 (0d) Not blocked
Routing (PBR) aiming at: 01 (1d) Not blocked
– Reducing the routing Inaccuracy problem effects 10 (2d) Blocked
11 (3d) Blocked
– Reducing (even removing) the amount of update
messages
• Main features: The PBR does not extract the
required network state information from the
update messages, instead it infers this information
from the behavior of previous connection
requests.
5
Workshop Vilanova July 7COMPUTER ARCHITECTURE DEPARTMENT Maximum Transmission Distance (MTD)
• Full transparency is not however always achievable in long
distance networks.
• In a semi-transparent network routes are divided into transparent
sub-routes between signal regenerators.
• MTD (Maximum Transmission Distance) is the maximum distance
a signal can be routed on without any regeneration1.
• Wavelengths are classified into three classes (gold, silver and
bronze), each one characterized by a different value of the MTD2 .
4500
4000
3500
[km
disttance[km]
3000
2500
distance
Gold class (d > 3500 km)
2000
Silver class (3000COMPUTER ARCHITECTURE DEPARTMENT MINCOD Routing Algorithm
• Minimum Coincidence and Distance 100 250
Algorithm (MINCOD) routing algorithm 50 100 150
100
precomputes k routes:
– Computes the end to end paths
considering the routes that have less 150 200 100 50 50 50
shared links and minimum distance. 50
50 50
• MINCOD steps to compute k-routes: 100 200 100
200
– Selectsthe shortest path (in distance) 100
– Computes the Minimum Shared Link 125
(MSL) of the rest of paths
Shortest Paths
MINCOD Paths
MSL =Distance* (SL +1) (Kms)
(Kms, SL)
Distance: distance of the path in Kms. 1-6-8-12-13 (400)
SL: number of links shared between each path 1-6-8-12-13
and the previous selected paths. 1-7-12-13 (450) (400,SL=0)
1-2-8-12-13 (600) 1-7-12-13
• Select the path with minimum MSL 1-2-3-4-9-11-14-12-13 (450, SL=1)
• Repeat the last two steps (k-2) times (725) 1-2-3-4-9-11-14-12-13
1-2-3-5-9-11-14-12-13 (725, SL=2)
(750)
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Workshop Vilanova July 7Prediction Routing according MTD
PR-MTD
COMPUTER ARCHITECTURE DEPARTMENT
K number of routes
W number of wavelengths
• The PR-MTD utilizes k SRi number of subroutes of route i
previously computed
For i=0 to K-1
routes by means of the For j=0 to W-1
MINCOD routing NotReachable=0;
algorithm For t=0 to SRi-1
If Distance(t) ≥ MTD(j) NotReachable=1;
EndFor
• It selects the first If NotReachable==0
If wavelength j available in all the links of route i
lightpath with two-bit ROUTE=i;
counter lower than 2, LAMBDA=j;
output link availability and break; // the lightpath is assigned
EndIf
accomplishing the MTD EndIf
constraint in each one of EndFor
the sub-routes. EndFor
8
Workshop Vilanova July 7COMPUTER ARCHITECTURE DEPARTMENT PR-MTD Performance Evaluation
Network Scenario:
• 2 fibres per link and 40 wavelengths per fibre.
• Wavelengths are divided into three classes, gold, silver and bronze, with MTD of 4000, 3500 and
3000 km respectively.
• Nodes Madrid, Barcelona, Paris, Dublin, Milan, Frankfurt, Amsterdam, Prague, Stockholm and
Athens act as source and destination; and nodes, Frankfurt, Amsterdam, Vienna, Milan, Prague and
Warsaw have regenerators.
• Comparing PR-MTD with SP-LL and MINCOD-LL (SP-LL and MINCOD-LL with periodically updating)
SP-LL(1)
0,4
SP-LL(5)
SP-LL(10)
0,35 2_MINCOD-LL(1)
2_MINCOD-LL(5)
% of Blocked Connections
0,3 2_MINCOD-LL(10)
2_PR-MTD
0,25
0,2
0,15
0,1
0,05
0
0 0,1 0,2 0,3 0,4 0,5 0,6
Erlangs
9
Workshop Vilanova July 7COMPUTER ARCHITECTURE DEPARTMENT Part 2
• Proposal of IA-RWA algorithm PR-Q taking into
account:
– Q Personik’s factor as physical impairment
– Inaccuracy in physical network state information (Q
value).
• Work developed in Nobel 2 Project
• Performance evaluation enhanced to be
submitted to IEEE Networks Special Issue
10
Workshop Vilanova July 7COMPUTER ARCHITECTURE DEPARTMENT Q Personik’s factor
• Work developed by M. Quagliotti and W.Erangoli from Telecom Italy and Corecom.
• Q Personik’s factor is a Quality factor that aggregates different physical impairment effects
Qend = a0 + a1OSNR end + a2 NL tot
OSNR is the optical signal to noise ratio expressed in dB
NL takes into account the effects of non-linearity
Q factor of this lightpath is 22.6 dB
Link 1: 128 km Link 3: 580 km
Link 2: 298km
Genova Milano Pisa Rome
a 1 2
b 1 2 3 4
c 1 2 3 4
e
5 6 7
node Amplifier distance: max 85 km
Line amplifier Span distance
pre-amplifier
booster
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Workshop Vilanova July 7COMPUTER ARCHITECTURE DEPARTMENT Planning Phase- Deterministic Algorithm
• Network planning, made according to a static traffic
pattern with a static (deterministc) RWA and
Regenerator Placement algorithm.
• Network is equipped with one regenerator per node and
one system (40 wavelengths) per link.
– Each permanent connection requests is routed following the
Shortest Path and First Fit RWA.
– Regeneration is needed if Qlightpath < Qthreshold (17 dB)
– After serving each request, the nodes with no more free
regenerators are provided with an extra regenerator and the
links with no more free wavelengths are provided with an
additional system.
• Additional check that removes unused
regenerators/systems.
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Workshop Vilanova July 7COMPUTER ARCHITECTURE DEPARTMENT Planning Phase- PanEuropean Results
• 2 traffic matrixes are used:
– a uniform matrix →fullmesh, 378 bidirectional connections
– polarized matrix →Traffic from year 2007, 741 bidirectional
connections (Nobel 2 Data)
• The line system installed on the links of the network are always 40
channels DWDM system at 10 Gbit/s
• Results from this static simulations give us for each case:
– Number of systems installed in each link
– Number of regenerators in each node
Required full
Required line system (40 λ
Traffic type
Regenerators each)
Uniform 129 56
Year 2007 197 79
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Workshop Vilanova July 7COMPUTER ARCHITECTURE DEPARTMENT Analysis Phase- Predictive Algorithm
• Prediction Route according to the Q factor (PR-Q):
– Based on the PBR mechanism.
– Takes into account the inaccuracy on the Q value.
• PR_Q selects the first route and corresponding
wavelengths (wavelength conversion is assumed in
nodes with regeneration) fulfilling:
– Qsub-route > Qthreshold
– 2 bit-counters of each sub-route (and wavelengths) < 2
– There is wavelength availability in all the sub-routes of the
lightpath
• If the selected ligthpath is:
– Blocked → 2-bit counter increased
– Established → 2 bit counter decreased
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Workshop Vilanova July 7COMPUTER ARCHITECTURE DEPARTMENT Simulation Framework
• Q_route is the Q factor of a lightpath computed by the IA-RWA using a
set of stored parameters, independently on the fact that the values are
right or wrong
• Q_actual is the real Q factor in the network
• Q_design is the Q factor considered by the planning algorithm
3 Simulation cases for Dynamic Traffic:
• Perfect Knowledge and Perfect matching (PKPM)
Q_route= Q_actual= Q_design
• Perfect Knowledge and Imperfect Matching (PKIM)
Q_route= Q_actual ≠Q_design
Q_route and Q_actual have pessimistic values (Q_design – 2 dB)
• Imperfect Knowledge and Imperfect Matching (IKIM)
Q_route ≠ Q_actual
Q_actual= Q_route – 2dB Q_actual (real) is worse than Q used by IARWA
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Workshop Vilanova July 7COMPUTER ARCHITECTURE DEPARTMENT Performance Evaluation
20%
PR-Q
Deterministic
Blocking Probabi 15%
Fullmesh Traffic
10%
Dynamic Traffic:
5%
1 Erlang between
every source
0% destination →
PKPM PKIM IKIM
378 Erlangs
10%
PR-Q
Deterministic
8%
Year 2007 Traffic
Blocking Probabi
6%
Dynamic Traffic:
4%
517 Erlangs
2%
0%
PKPM PKIM IKIM
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Workshop Vilanova July 7COMPUTER ARCHITECTURE DEPARTMENT Performance Evaluation
Fullmesh Traffic
18
16
14
Bloking probability (%)
12 PKPM PR-Q
PKIM PR-Q
10 IKIM PR_Q
PKPM Deterministic
8
PKIM Deterministic
6 IKIM Deterministic
4
2
0
1 2 3 4
Offered traffic source-destination (Erlang)
17
Workshop Vilanova July 7COMPUTER ARCHITECTURE DEPARTMENT Conclusions and Future Work
• We have introduced the Prediction Based Routing Mechanism in
the Impairment Aware RWA algorithms.
– Taking into account the inaccuracy in the wavelength availability
– Taking into account the inaccuracy in the Physical Impairment
information
• Routing algorithms inferred from the PBR performs better than
usual IA-RWA algorithms in case of inaccurate information
conditions.
• In DICONET project we continue working developing IA-RWA
algorithms:
– Not only based on PBR
– For the planning and the analysis cases
– Specifically designed for each PI (or different PIs)
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