Adapting Spectrum Resources using Predicted IP Trafﬁc in Optical

Networks

Constantine A. Kyriakopoulos

1 a

, Petros Nicopolitidis

1 b

, Georgios I. Papadimitriou

1 c

and Emmanouel Varvarigos

2 d

1

Dept. of Informatics, Aristotle University, Thessaloniki, Greece

2

School of Electrical and Computer Engineering, National Technical University of Athens, Greece

Keywords:

Optical Networks, Particle Swarm Optimisation, Linear Regression, Analytics, Load Balancing, Trafﬁc

Prediction.

Abstract:

Elastic optical networks provide the advantage of elaborate resource utilisation for achieving a wide range

of performance goals. Cross-layer optimisation is feasible by exploiting high layer IP trafﬁc prediction for

achieving efﬁcient lightpath establishment at the lower layer. Swarm Intelligence can provide a tool to adap-

tively allocate spectrum resources according to trafﬁc analytics from the IP layer. A new algorithm is designed

and evaluated that exploits these analytics using particle swarm optimisation to allocate spectrum.

1 INTRODUCTION

Optical backbone networks reliably cover a wide

range of current and possibly future connectivity

needs. Intelligent resource allocation in this ﬁeld un-

dergoes ongoing research efforts from the community.

Performance increases when resources are allocated

in an on-line fashion while the network is operating

(Kyriakopoulos et al., 2018), considering as input the

current trafﬁc demand and adapting to available spec-

trum resources facilitating it efﬁciently.

Various types of technology are enabled at the

optical layer. Elastic optical network (EON) (Jinno,

2016) platforms offer ﬂexibility for conﬁguring, since

the usage of variable-rate transponders can provide

the right amount of resources on demand. Orthogo-

nal frequency division multiplexing (OFDM) (Chat-

terjee et al., 2017), (Zhang et al., 2012a) is adequate

to provide support for variable-rate light connections.

This is achieved by utilising many subcarriers for data

transfers. The overlapping of spectra between these

subcarriers facilitates the compactness of available

resources due to their orthogonal modulation. This

design increases the overall efﬁciency. Bandwidth-

variable transponders (BVTs) (Moreolo et al., 2016)

a

https://orcid.org/0000-0001-7874-2205

b

https://orcid.org/0000-0002-5059-3145

c

https://orcid.org/0000-0001-9529-9380

d

https://orcid.org/0000-0002-4942-1362

embed the enabling technologies for achieving these

goals.

In a cross-layer network design (Sartzetakis et al.,

2018), a relation is formed between the physical and

network layers. This is a push-pull design where both

layers facilitate each other for achieving important

performance goals. Trafﬁc conditions taking place

at the IP layer may be exploited for efﬁciently estab-

lishing lightpaths at the physical layer. As an exam-

ple, connectivity between data centres follows spe-

ciﬁc trafﬁc patterns. Predicting the state and varia-

tion of these patterns, useful analytics can be provided

to the physical layer for establishing lightpaths hav-

ing the right amount of spectrum resources for im-

proving performance. In the opposite direction, im-

pairments in the physical layer are estimated (Bouda

et al., 2018), (Fludger and Kupfer, 2016), (Beletsioti

et al., 2018) and considered for creating connections

from the above layers.

An adaptive tool that solves many optimisation

problems and is capable of exploiting higher layer

analytics for improving its performance is particle

swarm optimisation (PSO) (Mohemmed et al., 2008),

(Zhang et al., 2015b). Its applications include neural

network training, pattern classiﬁcation and function

optimisation, among others. The main focus is the

emulation of animals’ social behaviour including in-

sects or birds. The main trait is that the individuals in-

side a group cooperate to ﬁnd food. Each member in

Kyriakopoulos, C., Nicopolitidis, P., Papadimitriou, G. and Varvarigos, E.

Adapting Spectrum Resources using Predicted IP Trafﬁc in Optical Networks.

DOI: 10.5220/0009819500530058

In Proceedings of the 17th International Joint Conference on e-Business and Telecommunications (ICETE 2020) - DCNET, OPTICS, SIGMAP and WINSYS, pages 53-58

ISBN: 978-989-758-445-9

Copyright

c

2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

53

the swarm repositions itself by keeping track of past

movements, either its own or from its neighbourhood.

This type of social behaviour is standardised and can

be used to solve difﬁcult computational problems. For

example, in a network graph topology, a population is

able to ﬁnd paths with speciﬁc traits like short dis-

tances, or any other metric that replaces the weight of

an edge in the graph. Complexity decreases when this

logic is applied to large topologies.

A popular tool for usage in network trafﬁc predic-

tion is linear regression (Mata et al., 2018). It is used

to ﬁt a predictive model to a set of values, e.g., in

a Cartesian system. This way, when more values are

collected in the x-axis, the corresponding values in the

y-axis are predicted. It is used to detect the strength

of the response and explanatory variables. Models of

the regression are ﬁtted by exploiting the least squares

method.

In related research (Morales et al., 2017) of this

ﬁeld, a virtual topology reconﬁguration mechanism is

proposed based on data analytics for trafﬁc prediction.

It uses a machine learning algorithm that relies on an

artiﬁcial neural network (ANN) for providing adap-

tive trafﬁc models. Data from trafﬁc prediction are

used in the reconﬁguration model and the problem is

solved utilising a heuristic method.

In this work, higher layer analytics are collected

by applying a linear regression method to predict fu-

ture trafﬁc demand. These analytics are provided to a

PSO method for allocating the appropriate amount of

spectrum resources in an online fashion. Performance

is improved since the found paths carry spare band-

width that can facilitate future trafﬁc demand. At the

same time, highly congested paths are avoided since

the PSO core adapts to those providing more spare

bandwidth to accommodate future demand. Accord-

ing to results, there is reduction in transponder num-

bers which leads to less power consumption. Also,

The percentage of valid paths to allocate resources

to is high and optical grooming is dominant at lower

rates. At the same time, path elongation is minor.

The rest of the text is organised as follows. Sec-

tion 2 describes the proposed Metis algorithm and

Section 3 presents the network environment that is

used to evaluate performance. Finally, in Section 4

simulation results are presented.

2 METIS ALGORITHM

Metis (a mythical titaness - mother of wisdom and

deep thought) relies on analytics from the IP layer to

establish lightpaths. Trafﬁc requests between node

pairs arrive sequentially. A history log of previous

Figure 1: Ring particle neighbourhood.

values is maintained and a window of these is used to

predict the next arrival rate for every node pair. The

predicted value comprises a parameter for the edge

weight that the PSO core will use to calculate the ap-

propriate path when establishing lightpaths.

The PSO core uses the physical topology connec-

tions with modiﬁed weights. This way, it tries to ﬁnd

paths avoiding routes where the load is predicted to

increase. The resulting resource allocation is more

balanced with low blocking probability, in compari-

son to the corresponding shortest path replacement.

Adaptivity to future load is a trait that renders Metis

suitable for online execution while network runtime

conditions vary.

PSO is a stochastic optimisation method (Zhang

et al., 2015b) that mimics the social behaviour of a

bird ﬂock, etc. The algorithmic ﬂow initiates with a

set of particles whose positions represent possible so-

lutions in the search space. The search for optimal po-

sitions leads to a solution by updating their velocities

in an iterative fashion. The ﬁtness of each particle is

calculated, and the one having the highest value pro-

vides the best solution in the search space. Each parti-

cle’s velocity depends on the current and best position

it had so far. Also, it depends on the best neighbour

position. After a number of iterations, the solution is

provided by the particle that converged faster.

Particle population is organised in a ring topology

(Figure 1). The number of particles affects the per-

centage of valid paths to ﬁnd. A larger number of

particles, in relation to large iteration number, pro-

vides more accurate results (using the appropriate ﬁt-

ness function) but increases the computational com-

plexity. This is due to more ﬁtness function evalu-

ations and candidate path constructions per particle.

Every particle alters its velocity and hence its posi-

tion according to the state of its neighbours.

Every incoming request is served by Metis which

is described in Algorithm 1. The ﬁrst preprocess-

DCNET 2020 - 11th International Conference on Data Communication Networking

54

Algorithm 1: Metis Abstract Pseudo Code.

New incoming spectrum allocation request

For every topology edge, use analytics as weight

Feed PSO core with the updated topology

direct ← f alse

path = psoRouter(src, dest)

if path.size() > 0 then

for all edge ∈ pathEdges do

optical ← f alse

if optical = optGrooming(edge) 6= 0 then

continue

else

direct = directLP(edge)

end if

end for

else

// Enter failsafe mode

Get shortest path from ’src’ to ’dest’

for all edge ∈ pathEdges do

direct = directLP(edge)

end for

end if

ing event relates to the use of Equation 1’s result as

weight for every topology’s edge (connection). Mul-

tiple data transfers exist between node-pairs. For ex-

ample, if the third request is to be served, the previous

two comprise the prediction window. If the ﬁrst two

are 10 and 20 Gbps, the predicted value for the next is

30 Gbps. The actual weight value to provide to PSO

core from Formula 1 is 35.

Next, the updated topology becomes PSO core’s

input for ﬁnding the appropriate path from initial node

to destination. If it succeeds, for every edge of the

path, optical grooming is attempted (Zhang et al.,

2012b). In this case, available transponder slices at

both ends of the edge comprise a newly established

lightpath (Kyriakopoulos et al., 2019). If no avail-

able slice(s) exist, one (or two) new transponder(s)

are used for creating the light connection.

It is possible for the PSO core not to ﬁnd a valid

path. This happens if the number of iterations or

the population size is not large enough. The failsafe

mode initiates, where direct lightpaths (using two new

transponders) are established upon every edge of the

shortest path between end nodes.

Weight =

(

rate

n

+

ˆ

rate

n+1

2

,

ˆ

rate

n+1

> rate

n

rate

n

, otherwise

(1)

y = a

0

+ a

1

x + a

2

x

2

+ a

3

x

3

+ ·· · + a

n

x

n

+ ε (2)

y

1

y

2

y

3

.

.

.

y

n

=

1 x

1

x

2

1

··· x

m

1

1 x

2

x

2

2

··· x

m

2

1 x

3

x

2

3

··· x

m

3

.

.

.

.

.

.

.

.

.

.

.

.

1 x

n

x

2

n

··· x

m

n

a

0

a

1

a

2

.

.

.

a

m

+

ε

1

ε

2

ε

3

.

.

.

ε

n

(3)

−→

y = X

−→

a +

−→

ε (4)

−→

a =

X

T

X

−1

X

T

−→

y (5)

In Equation 2, y represents

ˆ

rate

n+1

which is the

predicted edge bandwidth. The previous values from

rate

1

to rate

n

comprise the prediction window. Pro-

viding to x the slot n + 1, y is calculated (Seber and

Lee, 2012). Values between y

1

. . . y

n

comprise the

window of previous rates. Values between x

1

. . . x

n

comprise the window of previous slots. A new repre-

sentation for the equation is in Formula 3 with the

purpose of ﬁnding the array of coefﬁcients

−→

a . In

vector form is the Equation 4. Coefﬁcients are cal-

culated from Equation 5 by using ordinary least mean

square estimation. The result is used in Equation 2

to calculate the next rate value which is the predicted

value. The ε values represent possible minor errors

which are ignored and the result is named an esti-

mated value.

3 NETWORK ENVIRONMENT

The purpose is to allocate spectrum resources while

incoming requests arrive one-by-one. The problem is

formally described and follows next.

• A directed graph is used to describe the topology

of the elastic network, i.e., G(V, E). Speciﬁcally,

V is the set of nodes and E is the set of links.

• A set of frequency slices F are used for transpon-

der end-point connections for each link ∈ E. F =

{ f

1

, f

2

, ·· · , f

n

}, where n is the ceiling of connec-

tions per ﬁbre.

• A set of available modulation formats M =

{m

1

, m

2

, ·· · , m

n

}, where n is their maximum

number. Each format is described by a pair m =

h

f , r

i

, where f is the lightpath spectrum and r is

the optical reach.

• A set of trafﬁc demands D that reside in a matrix.

Each entry is described by a tuple d =

h

s, d, b

i

,

where s is the request’s source, d the destination

and b represents the bitrate.

20 interconnected nodes comprise a topology to

use for evaluating Metis’ performance. Figure 2 de-

picts the connections and the corresponding weights.

Modulation is based on the available choices of

Table 1. These choices are input to the modulation

policy when light connections are established using

available transponder slices. Distance is a factor that

limits the subcarrier capacity and is obeyed for ev-

ery new request’s bitrate. To choose the appropriate

Adapting Spectrum Resources using Predicted IP Trafﬁc in Optical Networks

55

Figure 2: 20-node topology (Mohemmed et al., 2008).

Table 1: Modulation Formats.

Format Subcarrier Capacity (Gbps) Distance (km)

BPSK 12.5 4000

QPSK 25 2000

8QAM 37.5 1000

16QAM 50 500

32QAM 62.5 250

64QAM 75 125

format, all these are sorted in descending order. The

value that is ceiling to incoming request’s rate is kept

by the policy.

Figure 3 contains the low level details of estab-

lishing the new lightpath λ

3

when λ

1

and λ

2

are al-

ready established. A prerequisite is the existence of

available transponder slices at source (left) and desti-

nation (right) nodes. The intermediate node consists

of a transmitter and a receiver.

4 RESULTS

The simulating environment consists of speciﬁc pa-

rameters that follow next. Variable-rate transponders

utilise up to 10 lightpath connections. Two adja-

cent frequency slots comprise a guardband. Avail-

able modulation formats are in Table 1 and each of

Figure 3: Optical grooming.

Figure 4: Transponders according to increased trafﬁc de-

mand.

them has its own spectrum range. A table of spectrum

values according to data rates is found in Reference

(Zhang et al., 2015a).

Trafﬁc demand values are generated by a random

function in the range [40, 2X − 40] Gbps, using steps

of 40 Gbps. Variable X ∈ {40, 80, 120, 160, 200}. 500

requests are established between uniformly selected

node-pairs. Past values of a node-pair comprise the

prediction window.

The PSO core relies on 750 iterations and popu-

lation size of 40, unless otherwise noted. The linear

regression prediction method uses a window of 5 pre-

vious trafﬁc values between each node-pair.

The simulating environment is designed and im-

plemented in Modern C++ with the Clang/LLVM 10

compiler, the aid of Boost graph library 1.67 and Ar-

madillo linear algebra library 9, on x64 Debian 10.

In Figure 4, the number of utilised transponders

increases according to trafﬁc demand. When its aver-

age value reaches 200 Gbps, there are many requests

that exceed the upper transponder limit of 400 Gbps,

so optical grooming is not feasible in this case. This

is the reason for the high performance of Metis at

lower rates. Its slight path elongation results in more

transponder usage at higher bitrates, in comparison

to the direct lightpath establishment that relies on the

shortest path between end nodes. When the average

rate reaches 200 Gbps, some values are close to the

maximum supported transponder rate which is 400

Gbps. So, optical grooming on existing transponders

is not feasible and new ones must be utilised on paths

not being shorter.

In Figure 5, the percentage of valid paths found

by the embedded PSO mechanism in Metis, is de-

picted according to the particle population size in-

crease. When the population is low, particles may not

DCNET 2020 - 11th International Conference on Data Communication Networking

56

Figure 5: PSO valid path percentage according to popula-

tion size.

Figure 6: Optical grooming ratio according to trafﬁc.

converge to a valid solution. At higher values (x axis),

almost every execution returns valid paths, so the per-

centage (y axis) reaches the value of 100%. The aver-

age request rate is 80 Gbps with 10 iterations between

particles. The percentage of failures for PSO to ﬁnd

paths can be considered as blocking probability for

the PSO core, since it then enters the failsafe mode

(Algorithm 1).

In Figure 6, the percentage of optically groomed

lightpaths is depicted according to the increasing av-

erage trafﬁc demand value. Since the availability of

optical grooming at values above 400 Gbps is non-

existent, the percentage keeps decreasing. Metis’ per-

formance is high between low and mid-range trafﬁc

values. From the grooming perspective, the percent-

age of direct lightpaths can be considered as blocking

probability.

In Figure 7, the effect of not utilising the short-

Figure 7: Hop-count according to increased trafﬁc demand.

est path between request end nodes is depicted. This

compensates due to the higher performance as de-

scribed in the previous graphs. Also, longer paths are

established for accommodating the prediction of IP

layer’s future trafﬁc demand.

5 CONCLUSIONS

Higher layer analytics are exploited for improving the

efﬁciency of the lightpath establishment procedures at

the lower layer. The particle swarm optimisation core

exploits the predicted future trafﬁc demand and ﬁnds

paths with higher available spectrum resources. The

adaptivity to future IP trafﬁc leads to higher overall

performance, in relation intelligent spectrum alloca-

tion.

ACKNOWLEDGEMENTS

This research was co-ﬁnanced by the European Union

and Greek National Funds through the Operational

Program Competitiveness, Entrepreneurship and In-

novation, under the call RESEARCH-CREATE-

INNOVATE (project code: T1EDK-05061).

REFERENCES

Abkenar, F. S. and Rahbar, A. G. (2017). Study and analysis

of routing and spectrum allocation (rsa) and routing,

modulation and spectrum allocation (rmsa) algorithms

in elastic optical networks (eons). Optical Switching

and Networking, 23:5–39.

Adapting Spectrum Resources using Predicted IP Trafﬁc in Optical Networks

57

Beletsioti, G. A., Papadimitriou, G. I., Nicopolitidis, P.,

and Miliou, A. N. (2018). Earthquake tolerant en-

ergy aware algorithms: A new approach to the design

of wdm backbone networks. IEEE Transactions on

Green Communications and Networking, 2(4):1164–

1173.

Bouda, M., Oda, S., Vassilieva, O., Miyabe, M., Yoshida,

S., Katagiri, T., Aoki, Y., Hoshida, T., and Ikeuchi, T.

(2018). Accurate prediction of quality of transmission

based on a dynamically conﬁgurable optical impair-

ment model. Journal of Optical Communications and

Networking, 10(1):A102–A109.

Chatterjee, B. C., Ba, S., and Oki, E. (2017). Fragmenta-

tion problems and management approaches in elastic

optical networks: A survey. IEEE Communications

Surveys & Tutorials, 20(1):183–210.

Deepa, O. and Senthilkumar, A. (2016). Swarm intelli-

gence from natural to artiﬁcial systems: Ant colony

optimization. Networks (Graph-Hoc), 8(1):9–17.

Fludger, C. R. and Kupfer, T. (2016). Transmitter im-

pairment mitigation and monitoring for high baud-

rate, high order modulation systems. In ECOC 2016;

42nd European Conference on Optical Communica-

tion, pages 1–3. VDE.

Jinno, M. (2016). Elastic optical networking: Roles and

beneﬁts in beyond 100-gb/s era. Journal of Lightwave

Technology, 35(5):1116–1124.

Kyriakopoulos, C. A., Nicopolitidis, P., Papadimitriou,

G. I., and Varvarigos, E. (2019). Fast energy-efﬁcient

design in elastic optical networks based on signal

overlap. IEEE Access, 7:113931–113941.

Kyriakopoulos, C. A., Papadimitriou, G. I., and Nicopoli-

tidis, P. (2018). Towards energy efﬁciency in vir-

tual topology design of elastic optical networks.

International Journal of Communication Systems,

31(13):e3727.

Mata, J., De Miguel, I., Duran, R. J., Merayo, N., Singh,

S. K., Jukan, A., and Chamania, M. (2018). Artiﬁcial

intelligence (ai) methods in optical networks: A com-

prehensive survey. Optical Switching and Networking,

28:43–57.

Mohemmed, A. W., Sahoo, N. C., and Geok, T. K. (2008).

Solving shortest path problem using particle swarm

optimization. Applied Soft Computing, 8(4):1643–

1653.

Morales, F., Ruiz, M., Gifre, L., Contreras, L. M., L

´

opez,

V., and Velasco, L. (2017). Virtual network topology

adaptability based on data analytics for trafﬁc predic-

tion. IEEE/OSA Journal of Optical Communications

and Networking, 9(1):A35–A45.

Moreolo, M. S., Fabrega, J. M., Nadal, L., V

´

ılchez, F. J.,

Mayoral, A., Vilalta, R., Mu

˜

noz, R., Casellas, R.,

Mart

´

ınez, R., Nishihara, M., et al. (2016). Sdn-

enabled sliceable bvt based on multicarrier technology

for multiﬂow rate/distance and grid adaptation. Jour-

nal of Lightwave Technology, 34(6):1516–1522.

Sartzetakis, I., Christodoulopoulos, K., and Varvarigos, E.

(2018). Cross-layer adaptive elastic optical networks.

IEEE/OSA Journal of Optical Communications and

Networking, 10(2):A154–A164.

Seber, G. A. and Lee, A. J. (2012). Linear regression anal-

ysis, volume 329. John Wiley & Sons.

Zhang, G., De Leenheer, M., Morea, A., and Mukherjee, B.

(2012a). A survey on ofdm-based elastic core optical

networking. IEEE Communications Surveys & Tuto-

rials, 15(1):65–87.

Zhang, G., De Leenheer, M., and Mukherjee, B. (2012b).

Optical trafﬁc grooming in ofdm-based elastic optical

networks. Journal of Optical Communications and

Networking, 4(11):B17–B25.

Zhang, J., Zhao, Y., Yu, X., Zhang, J., Song, M., Ji, Y.,

and Mukherjee, B. (2015a). Energy-efﬁcient trafﬁc

grooming in sliceable-transponder-equipped ip-over-

elastic optical networks [invited]. Journal of Optical

Communications and Networking, 7(1):A142–A152.

Zhang, Y., Wang, S., and Ji, G. (2015b). A comprehensive

survey on particle swarm optimization algorithm and

its applications. Mathematical Problems in Engineer-

ing, 2015.

DCNET 2020 - 11th International Conference on Data Communication Networking

58