1. Accelerating the Design of
Optical Networks using
Surrogate Models
IV INTERNATIONAL WORKSHOP ON TRENDS IN OPTICAL TECHNOLOGIES – PROF. CARMELO J. A. BASTOS-FILHO, PHD
Carmelo J. Bastos-Filho (Assoc. Prof. UPE),
Danilo R. B. Araújo (Ph.D. Student, UFPE)
Erick A. Barboza (Ph.D. Student, UFPE)
Joaquim F. Martins-filho (Assoc. Prof. UFPE)

2. Major question for this presentation
Is it possible to bring knowledge from other
areas to improve the solutions for optical
networks?
IV INTERNATIONAL WORKSHOP ON TRENDS IN OPTICAL TECHNOLOGIES – PROF. CARMELO J. A.
BASTOS-FILHO, PHD

3. In order to answer the general question, we will try to
answer more specific questions!
How to evaluate the current available methods to assess optical networks?
• An overview about the trade-off between accuracy and performance of the available tools
What is machine learning?
• A brief overview on Artificial Neural Networks
• What kind of applications we can develop?
What is Network Science?
• A brief introduction to network metrics and generative models
How can we develop surrogate models to assess optical networks?
• The major challenges related to the use of alternative procedures to assess optical networks
• Network sciences + Artificial Neural Nets + Physical layer information  Can we develop a suitable
surrogate model to assess optical networks???
What is the impact of using these surrogate models to design optical
networks?
• A comparative study between “traditional” approaches and surrogate-based approaches
IV INTERNATIONAL WORKSHOP ON TRENDS IN OPTICAL TECHNOLOGIES – PROF. CARMELO J. A. BASTOS-FILHO, PHD

4. Designing Optical Networks
In order to design optical networks, one must define:
1. The physical topology;
2. The equipments to be deployed (amplifiers, ROADMs, number of
TX cards);
3. The deployed modulation format, grooming scheme, etc., ….
IV INTERNATIONAL WORKSHOP ON TRENDS IN OPTICAL TECHNOLOGIES – PROF. CARMELO J. A.
BASTOS-FILHO, PHD

5. Designing Optical Networks
In order to design optical networks, one must define:
1. The physical topology;
2. The equipments to be deployed (amplifiers, ROADMs, number of
TX cards);
3. The deployed modulation format, grooming scheme, etc., ….
IV INTERNATIONAL WORKSHOP ON TRENDS IN OPTICAL TECHNOLOGIES – PROF. CARMELO J. A.
BASTOS-FILHO, PHD
This means that you have a lot of variables!!!!

6. Designing Optical Networks
(Let’s try to simplify)
One has to define:
• Which nodes should be connected [a1,2; a1,3; ... ; an-1,n]?
• ai,j=1 if i and j are connected, and ai,j=0 otherwise;
• Which type of amplifier should be deployed in each link [amp1,2; amp1,3; ... ; ampn-1,n]?
• ampi,j can assume different labels depending on the availability and suitability;
• How many wavelengths must be available in each link [w1,2; w1,3; ... ; wn-1,n]?
• wi,j is the number of wavelengths between node i and j;
• Which equipments should be installed in each node [ROADM1; ROADM2; ... ; ROADMn]?
Even if we try to simplify even more by using the same type of amplifier and ROADM in the
entire network, and we deploy the same number of wavelengths for all links
◦ We still have (n2-n)/2 + 2 variables
◦ [a1,2; a1,3; ... ; an-1,n; ROADM; w]
IV INTERNATIONAL WORKSHOP ON TRENDS IN OPTICAL TECHNOLOGIES – PROF. CARMELO J. A.
BASTOS-FILHO, PHD

7. Examples using this description
IV INTERNATIONAL WORKSHOP ON TRENDS IN OPTICAL TECHNOLOGIES – PROF. CARMELO J. A. BASTOS-FILHO, PHD
For n = 14  |X| = 93
The number of variables grows quickly when larger networks are used

8. Examples using this description
IV INTERNATIONAL WORKSHOP ON TRENDS IN OPTICAL TECHNOLOGIES – PROF. CARMELO J. A. BASTOS-FILHO, PHD
For n = 14  |X| = 93
For n = 34  |X| = 563
The number of variables grows quickly when larger networks are used

9. How to find the best “configuration”?
IV INTERNATIONAL WORKSHOP ON TRENDS IN OPTICAL TECHNOLOGIES – PROF. CARMELO J. A. BASTOS-FILHO, PHD
OPTIMIZATION METHODS!!!!
EXAMPLES:
Integer Linear Programming;
Evolutionary Algorithms;
Swarm Intelligence;
Multi-objective optimization.

10. How to find the best “configuration”?
IV INTERNATIONAL WORKSHOP ON TRENDS IN OPTICAL TECHNOLOGIES – PROF. CARMELO J. A. BASTOS-FILHO, PHD
The state-of-art optimization algorithms
can not garantee the optimum solution
for such a high dimensionality!!!!
OPTIMIZATION METHODS!!!!
EXAMPLES:
Integer Linear Programming;
Evolutionary Algorithms;
Swarm Intelligence algorithms;
Multi-objective optimization.

11. How to find the best “configuration”?
IV INTERNATIONAL WORKSHOP ON TRENDS IN OPTICAL TECHNOLOGIES – PROF. CARMELO J. A. BASTOS-FILHO, PHD
Besides, in all cases it is mandatory to have metrics to guide
the optimization process!
Objective functions:
CAPEX;
OPEX;
Energy consumption;
Network performance metrics.

12. How to find the best “configuration”?
IV INTERNATIONAL WORKSHOP ON TRENDS IN OPTICAL TECHNOLOGIES – PROF. CARMELO J. A. BASTOS-FILHO, PHD
Network performance metrics
Examples:
Blocking Probability (in dynamic traffic networks)
Utilization rate (for static networks)
Etc…

13. How to evaluate the “Objective
function”?Fidelity
Resource efficiency
Experimental
Measures
IV INTERNATIONAL WORKSHOP ON TRENDS IN OPTICAL TECHNOLOGIES – PROF. CARMELO J. A. BASTOS-FILHO, PHD
Fidelity of the analysis in comparison with real data
Resource efficiency in terms of financial or computational costs

14. How to evaluate the “Objective
function”?Fidelity
Resource efficiency
Experimental
Measures
Simulations Based on
Numerical Models
IV INTERNATIONAL WORKSHOP ON TRENDS IN OPTICAL TECHNOLOGIES – PROF. CARMELO J. A. BASTOS-FILHO, PHD
Fidelity of the analysis in comparison with real data
Resource efficiency in terms of financial or computational costs

15. How to evaluate the “Objective
function”?Fidelity
Resource efficiency
Experimental
Measures
𝑦 = 𝑓(𝑥)Simulations Based on
Numerical Models
Simulations Based on
Analytical Models
IV INTERNATIONAL WORKSHOP ON TRENDS IN OPTICAL TECHNOLOGIES – PROF. CARMELO J. A. BASTOS-FILHO, PHD
Fidelity of the analysis in comparison with real data
Resource efficiency in terms of financial or computational costs

16. How to evaluate the “Objective
function”?Fidelity
Resource efficiency
Experimental
Measures
𝑦 = 𝑓(𝑥)Simulations Based on
Numerical Models
Simulations Based on
Analytical Models
IV INTERNATIONAL WORKSHOP ON TRENDS IN OPTICAL TECHNOLOGIES – PROF. CARMELO J. A. BASTOS-FILHO, PHD
Fidelity of the analysis in comparison with real data
Resource efficiency in terms of financial or computational costs
Is there any
other
possibility??

17. How to evaluate the “Objective
function”?Fidelity
Resource efficiency
Experimental
Measures
Surrogate Models Based
on Machine Learning
𝑦 = 𝑓(𝑥)Simulations Based on
Numerical Models
Simulations Based on
Analytical Models
IV INTERNATIONAL WORKSHOP ON TRENDS IN OPTICAL TECHNOLOGIES – PROF. CARMELO J. A. BASTOS-FILHO, PHD
Fidelity of the analysis in comparison with real data
Resource efficiency in terms of financial or computational costs

18. How to evaluate the current available
methods for optical networks analysis?Fidelity
Resource efficiency
Experimental
Measures
Surrogate Models Based
on Machine Learning
𝑦 = 𝑓(𝑥)Simulations Based on
Numerical Models
Simulations Based on
Analytical Models
IV INTERNATIONAL WORKSHOP ON TRENDS IN OPTICAL TECHNOLOGIES – PROF. CARMELO J. A. BASTOS-FILHO, PHD
How to improve the fidelity
of surrogate models based
on machine learning?
?

19. What should we know to develop good
machine learning surrogates?
Network Science
◦Metrics
◦Generative models
Machine learning techniques
◦Artificial Neural Networks
IV INTERNATIONAL WORKSHOP ON TRENDS IN OPTICAL TECHNOLOGIES – PROF. CARMELO J. A.
BASTOS-FILHO, PHD

20. What is Network Science?
[1] T. G. LEWIS. NETWORK SCIENCE - THEORY AND APPLICATIONS. JOHN WILEY & SONS, 2009.
Network science is a multidisciplinary research field that can
be applied to any problem modeled by graphs, in which the
inputs or the topology of the graph can vary along the time
Recent developments in Network Science include:
• Proposal of metrics that can explain the structure and the behaviour of
real world networks
• Proposal of generative models that can represent the topology structures
of real world networks

21. What is Network Science?
[1] T. G. LEWIS. NETWORK SCIENCE - THEORY AND APPLICATIONS. JOHN WILEY & SONS, 2009.
Some well known metrics:
• Average path length (APL): the average of the minimum path for all pairs of
nodes (source, destination) – mean value of the shortest path routes
• Algebraic connectivity (AC): second smaller eigenvalue of the Laplacian matrix
– it is related to the robustness of the network
• Density (d): ratio between the number of established links and the maximum
number of possible links
• Diameter (D): the longest shortest path
• Entropy (I): measures the uncertainty regarding the degree of a given node
• Clustering coefficient (CC): it is calculated based on the number of
triangulations between nodes

22. What is Network Science?
[1] T. G. LEWIS. NETWORK SCIENCE - THEORY AND APPLICATIONS. JOHN WILEY & SONS, 2009.
Some well known generative models:
• K-Regular: consists in linking each node i with the following k nodes
• entropy equal to zero and the diameter/APL depend on k
• Ring networks are a special case

23. What is Network Science?
[1] T. G. LEWIS. NETWORK SCIENCE - THEORY AND APPLICATIONS. JOHN WILEY & SONS, 2009.
Some well known generative models:
• K-Regular: consists in linking each node i with the following k nodes
• entropy equal to zero and the diameter/APL depend on k
• Erdos-Renyi (ER): a link between i and j is randomly established, according to the
probability p
• High entropy, lower APL/CC
• Not applied to real world networks

24. What is Network Science?
[1] T. G. LEWIS. NETWORK SCIENCE - THEORY AND APPLICATIONS. JOHN WILEY & SONS, 2009.
Some well known generative models:
• K-Regular: consists in linking each node i with the following k nodes
• entropy equal to zero and the diameter/APL depend on k
• Erdos-Renyi (ER): a link between i and j is randomly established, according
to the probability p
• High entropy, lower APL/CC
• Watts-Strogatz (WS): starts with k-regular networks and performs rewiring
processes with the probability rp
• Lower entropy, low APL and high CC
• It can be suitable for transport networks!

25. What is Network Science?
[1] T. G. LEWIS. NETWORK SCIENCE - THEORY AND APPLICATIONS. JOHN WILEY & SONS, 2009.
Some well known generative models:
• K-Regular: consists in linking each node i with the following k nodes
• entropy equal to zero and the diameter/APL depend on k
• Erdos-Renyi (ER): a link between i and j is randomly established, according to the
probability p
• High entropy, lower APL/CC
• Watts-Strogatz (WS): starts with k-regular networks and after that rewires new
connections with the probability rp
• Lower entropy, low APL and high CC
• Barabási-Albert (BA): starts with 3 nodes fully connected and each new nodes
is added by using the preferential attachment concept (hubs attracts new
connections)
• High entropy and lower APL/diameter
• presence of hubs – can be used for access networks

26. Artificial Neural Networks
It is not a “magical” black box tool, instead it is a distributed tool for
function approximation
◦ It was demonstrated some decades ago
Each neuron applies a non-linear function over the weighted sum of the
inputs
If <the number of inputs forms a complete set regarding the required
output> and <there are enough neurons in the hidden layer> and <the
number of patterns presented to adjust the weights of the neurons is
enough> then
◦ <an ANN can be used to approximate one desired measure, i.e. the
output>
◦ *there are some well known algorithms to train the ANN. We used the
backpropagation one (widely and most used)
IV INTERNATIONAL WORKSHOP ON TRENDS IN OPTICAL TECHNOLOGIES – PROF. CARMELO J. A.
BASTOS-FILHO, PHD

27. ANN Applications for Optical Networks
IV INTERNATIONAL WORKSHOP ON TRENDS IN OPTICAL TECHNOLOGIES – PROF. CARMELO J. A.
BASTOS-FILHO, PHD

28. •We can use Multi-Layer Perceptron to map the
NF and GF as a function of the input and output
powers applied to the amplifier.
•MLPs may avoid the necessity of a small step to
obtain a high resolution characterization.
•One can measure operation points with a
gain interval of 3 dB, which results presenting
errors as low as of 0.1 dB.

29. ANN Applications for Optical Cognitive Networks
We have developed an
approach to adjust the
operating point of a cascade of
amplifiers (6 in a row for the
results in the figure) based on
the delta rule deployed to train
the ANN
IV INTERNATIONAL WORKSHOP ON TRENDS IN OPTICAL TECHNOLOGIES – PROF. CARMELO J. A.
BASTOS-FILHO, PHD

30. Let’s get back to
Surrogates
IV INTERNATIONAL WORKSHOP ON TRENDS IN OPTICAL TECHNOLOGIES – PROF. CARMELO J. A.
BASTOS-FILHO, PHD

31. How can we define a surrogate model to
assess optical networks?
We handle the following problem:
• Given: a RWA algorithm, the fiber topology and the
specification of the optical devices
• Goal: To estimate the blocking probability (BP);
• Subject to: the lack of an available wavelength or
unacceptable QoT due to physical impairments.
IV INTERNATIONAL WORKSHOP ON TRENDS IN OPTICAL TECHNOLOGIES – PROF. CARMELO J. A.
BASTOS-FILHO, PHD

32. Some possible surrogates to estimate BP:
• Simulations based on Monte Carlo experiments based on
discrete events
• It can be precise, but it needs a lot of time!
IV INTERNATIONAL WORKSHOP ON TRENDS IN OPTICAL TECHNOLOGIES – PROF. CARMELO J. A.
BASTOS-FILHO, PHD
How can we define a surrogate model to
assess optical networks?

33. Some possible surrogates to estimate BP:
• Simulations based on Monte Carlo experiments based on
discrete events
• It can be precise, but it needs a lot of time!
• Closed analytical expressions to estimate BP
• Fast, but can not represent all practical situations!
IV INTERNATIONAL WORKSHOP ON TRENDS IN OPTICAL TECHNOLOGIES – PROF. CARMELO J. A.
BASTOS-FILHO, PHD
How can we define a surrogate model to
assess optical networks?

34. Some possible surrogates to estimate BP:
• Simulations based on Monte Carlo experiments that evaluate QoT
by using analytical expressions
• It can be precise, but needs a lot of time!
• Closed analytical expressions to estimate BP
• Quick, but not precise!
• Artificial Neural Networks (ANNs) obtained by using a database of
previously evaluated optical networks
IV INTERNATIONAL WORKSHOP ON TRENDS IN OPTICAL TECHNOLOGIES – PROF. CARMELO J. A.
BASTOS-FILHO, PHD
How can we define a surrogate model to
assess optical networks?

35. How can we define a surrogate model to
assess optical networks?
IV INTERNATIONAL WORKSHOP ON TRENDS IN OPTICAL TECHNOLOGIES – PROF. CARMELO J. A. BASTOS-FILHO, PHD
PROPOSAL:
• Artificial Neural Networks as an approximation tool
• Networks science metrics to “catch” the network behaviour
• General physical layer information to include general
information regarding QoT
Output
Layer
Hidden LayerInput Layer
X1
X2
Xp
Z1
Z2
ZM
BP
......

36. How can we define a surrogate model to
assess optical networks?
Output LayerHidden LayerInput Layer
X1
X2
Xp
Z1
Z2
ZM
BP
......
IV INTERNATIONAL WORKSHOP ON TRENDS IN OPTICAL TECHNOLOGIES – PROF. CARMELO J. A. BASTOS-FILHO, PHD
First challenge: How to define the input set X?

37. How can we use
surrogates to
assess
networks?
Our proposed methodology is
based on combining PCA and
best selection
D. R. B. ARAUJO, C. J. A.
BASTOS-FILHO, J. F. MARTINS-
FILHO, METHODOLOGY TO
OBTAIN A FAST AND ACCURATE
ESTIMATOR FOR BLOCKING
PROBABILITY OF OPTICAL
NETWORKS, JOURNAL OF
OPTICAL COMMUNICATIONS
AND NETWORKING. 7 (5)
(2015) 380-391.
Begin
End
1. Select a superset of input
variables
2. Create a random dataset of
WRONs
3. Evaluate the dataset of WRONs
by simulations
5. Define p = 2
8. Use the p variables and the
ANN to estimate BP
6. Test all sets of p variables as
inputs of the ANN
ΔMSE > 0.05
7. Define p = p + 1
No
Yes
4. Use PCA to remove redundant
variables

38. How can we use
surrogates to
assess
networks?
The role of each part of our complete
solution [2]
[2] D. R. B. ARAUJO, C. J. A. BASTOS-FILHO, J. F. MARTINS-FILHO, METHODOLOGY TO OBTAIN A FAST AND ACCURATE ESTIMATOR FOR
BLOCKING PROBABILITY OF OPTICAL NETWORKS, JOURNAL OF OPTICAL COMMUNICATIONS AND NETWORKING. 7 (5) (2015) 380{391.
Evaluator
Learning Engine
Complex Networks Engine
Start
evaluation
Calculate the
ANN outcome
Compute
inputs
Start training
Discrete event
simulator (network
simulator)
Calculate the
weights of
ANN
Create a
dataset of
WRONs
WRON BP
inputs
dataset
Trained
ANN
WRON
WRON
Topological
properties
BP
Topological
properties
WRON

39. How can we use
surrogates to
assess
networks?
Superset of variables used to build an
accurate estimator for BP of WRONs
IV INTERNATIONAL WORKSHOP ON TRENDS IN OPTICAL TECHNOLOGIES – PROF. CARMELO J. A.
BASTOS-FILHO, PHD
Index Variable Definition
X1 W Number of wavelengths
X2 δ OXC isolation factor
X3 CC Clustering coefficient
X4 d Density
X5 Entropy of the DFT of the Laplacian eigenvalues
X6 AC Algebraic connectivity
X7 NC Natural connectivity
X8 Average degree
X9 APL Average path length (hops)
X10 D Diameter (hops)
X11 I(G) Entropy
X12 Dkm Diameter (km)
X13 APLkm Average path length (km)
X14 ρ Spectral radius
X15 CR Concentration of routes
X16 L Traffic load
X17 σPL Standard deviation of the minimum path lengths
X18 d(km) Fiber link density
X19 ∆OSNR Average OSNR margin
X20 σ∆OSNR Standard deviation of the ∆OSNR

40. An illustrative case study on the
performance of our proposal
IV INTERNATIONAL WORKSHOP ON TRENDS IN OPTICAL TECHNOLOGIES – PROF. CARMELO J. A. BASTOS-FILHO, PHD
13
12
14
10
6
4
1
2 9
11
8
7
5
3
100 km

41. How can we use surrogates to assess
networks?
[2] D. R. B. ARAUJO, C. J. A. BASTOS-FILHO, J. F. MARTINS-FILHO, METHODOLOGY TO OBTAIN A FAST AND ACCURATE ESTIMATOR FOR BLOCKING PROBABILITY
OF OPTICAL NETWORKS, JOURNAL OF OPTICAL COMMUNICATIONS AND NETWORKING. 7 (5) (2015) 380{391.
An illustrative case study about the performance of our proposal [2]
p Camada de
entrada
𝑴𝑺𝑬 𝝈 𝑴𝑺𝑬 𝚫𝑴𝑺𝑬
2 W, δ 4.02E-3 4.6E-5 -
3 W, δ, d 5.62E-4 2.4E-4 0.86
4 W, δ, d, CR 3.23E-4 1.6E-5 0.43
5 W, δ, d, CR, CC 2.83E-4 1.5E-5 0.12
6 W, δ, d, CR, CC,
𝐼(ℱ)
2.66E-4 6.8E-6 0.06
7 W, δ, d, CR, CC,
𝐼(ℱ), APL (km)
2.57E-4 9.7E-6 0.03
1.0E-04
2.0E-04
3.0E-04
4.0E-04
5.0E-04
6.0E-04
p = 3 p = 4 p = 5 p = 6 p = 7 Results
of [8]
SIMTON
A
SIMTON
B
MSE
Method to estimation of BP

42. What is the impact of surrogates and network
science to design optical networks?
[3] D. R. B. ARA´UJO, C. J. A. BASTOS-FILHO, AND J. F. MARTINS-FILHO. NA EVOLUTIONARY APPROACH WITH SURROGATE MODELS AND NETWORK SCIENCE CONCEPTS TO DESIGN OPTICAL NETWORKS.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 43(08):67–80, 2015.
We have successfully used surrogates and network science concepts to design optical networks
The two main fields under investigation are:
• Proposal of generative procedures to create good fiber topologies
• Proposal of schemes to combine surrogates and discrete event simulatior to accelerate the
convergence of EA-based approaches
We studied the impact of our proposal to design the 14-node network [3]
• Our goal is to find network configurations that presents good trade-off in terms of CAPEX
and blocking probability
• We compared our proposal with traditional EA-based approaches
• Previous approaches used random generators and used only network simulations to
assess the quality of network configurations

43. Watt-Strogatz model driven by traffic for
seed generation
IV INTERNATIONAL WORKSHOP ON TRENDS IN OPTICAL TECHNOLOGIES – PROF. CARMELO J. A.
BASTOS-FILHO, PHD

44. What is the impact of surrogates and network
science to design optical networks?
0
5000
10000
15000
20000
0.0001 0.001 0.01 0.1 1
Cost
Blocking Probability
CHAVES [7]
ARAUJO [8]
WS-T
[7] D. A. R. CHAVES, C. J. A. BASTOS-FILHO, J. F. MARTINS-FILHO, MULTIOBJECTIVE PHYSICAL TOPOLOGY DESIGN OF ALL-OPTICAL NETWORKS CONSIDERING QOS AND CAPEX, OPTICAL FIBER COMMUNICATION. OFC 2010, 1{3.
[8] D. R. B. ARAUJO, C. J. A. BASTOS-FILHO, E. A. BARBOZA, D. A. R. CHAVES, J. F. MARTINS-FILHO, AN ECIENT MULTI-OBJECTIVE EVOLUTIONARY OPTIMIZER TO DESIGN ALL-OPTICAL NETWORKS CONSIDERING PHYSICAL IMPAIRMENTS AND CAPEX, IN:
INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS (ISDA), 2011 11TH INTERNATIONAL CONFERENCE ON, 2011, PP. 76{81.

45. Cascade of Surrogate Models (CSM)
IV INTERNATIONAL WORKSHOP ON TRENDS IN OPTICAL TECHNOLOGIES – PROF. CARMELO J. A.
BASTOS-FILHO, PHD

46. 0
2000
4000
6000
8000
10000
12000
0.00001 0.0001 0.001 0.01 0.1 1
Cost(m.u.)
Blocking Probability
Pref
EA-NS
EA-CSM
IV INTERNATIONAL WORKSHOP ON TRENDS IN OPTICAL TECHNOLOGIES – PROF. CARMELO J. A. BASTOS-FILHO, PHD
Not using
surrogates
A reference Pareto front and two fronts obtained after 15 minutes of execution for EA-NS
and for our new proposal. Pareto fronts are for the non-uniform traffic scenario. The cost is
given in generic monetary units (m.u.).

47. 0
2000
4000
6000
8000
10000
12000
0.00001 0.0001 0.001 0.01 0.1 1
Cost(m.u.)
Blocking Probability
Pref
EA-NS
EA-CSM
C
B
D
A
IV INTERNATIONAL WORKSHOP ON TRENDS IN OPTICAL TECHNOLOGIES – PROF. CARMELO J. A. BASTOS-FILHO, PHD
A reference Pareto front and two fronts obtained after 15 minutes of execution for EA-NS
and for our new proposal. Pareto fronts are for the non-uniform traffic scenario. The cost is
given in generic monetary units (m.u.).

48. 0
2000
4000
6000
8000
10000
12000
0.00001 0.0001 0.001 0.01 0.1 1
Cost(m.u.)
Blocking Probability
Pref
EA-NS
EA-CSM
C
B
D
A
IV INTERNATIONAL WORKSHOP ON TRENDS IN OPTICAL TECHNOLOGIES – PROF. CARMELO J. A. BASTOS-FILHO, PHD
Not using
surrogates
A reference Pareto front and two fronts obtained after 15 minutes of execution for EA-NS
and for our new proposal. Pareto fronts are for the non-uniform traffic scenario. The cost is
given in generic monetary units (m.u.).
EXECUTION TIME

49. Conclusions
IV INTERNATIONAL WORKSHOP ON TRENDS IN OPTICAL TECHNOLOGIES – PROF. CARMELO J. A. BASTOS-FILHO, PHD
Current solutions for analysis and design of optical networks present a trade-off in terms of
fidelity and resource efficiency
Network science is a promising research field that can contribute to the development of new
network analysis tools
Global performance metrics for optical networks such as blocking probability can be assessed
by surrogate models based on machine learning techniques
Topological metrics from network science that summarize the fiber topology are natural
candidates to offer reduction of dimensionality for networks assessment
ANNs can be used to forecast the blocking probability of optical networks when the right set of
inputs is chosen
Surrogates and generative models can be used together to assist the design of optical networks

50. Thanks for your
attention!
IV INTERNATIONAL WORKSHOP ON TRENDS IN OPTICAL TECHNOLOGIES – PROF. CARMELO J. A.
BASTOS-FILHO, PHD

51. Accelerating the Design of
Optical Networks using
Surrogate Models
IV INTERNATIONAL WORKSHOP ON TRENDS IN OPTICAL TECHNOLOGIES – PROF. CARMELO J. A. BASTOS-FILHO, PHD
Carmelo J. Bastos-FILHO (Assoc. Prof. UPE),
Danilo R. B. Araújo (Ph.D. Student, UFPE)
Erick A. Barboza (Ph.D. Student, UFPE)
Joaquim F. Martins-filho (Assoc. Prof. UFPE)