Power-aware Algorithms for Energy-efﬁcient Elastic Optical Backbone

and Metro Networks

Georgia A. Beletsioti

1 a

, Stathis Mavridopoulos

1 b

, Georgios A. Tziroglou

1 c

Constantine A. Kyriakopoulos

1 d

, Georgios I. Papadimitriou

1 e

, Petros Nicopolitidis

1 f

and Emmanouel Varvarigos

2 g

Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, GR-54124, Greece

School of Electrical and Computer Engineering, National Technical University of Athens, Athens, GR-15780, Greece

Keywords: Elastic Optical Networks, Energy Efﬁciency, IP Over EON.

Abstract:

Research in Optical Networking has recently focused on Elastic Optical Network architectures, that support

elastic band connections to increase spectrum availability, support high transmission rates and reduce network

costs. Elastic optical networks offer ﬂexibility in the way capacity is assigned to connections and are con-

sidered the most prevalent solution for the next generation metro/backbone networks. Reduction in energy

consumption is an important issue in such networks. In this work, a new power aware algorithm is introduced,

which selectively switches off network links under low utilization scenarios supporting energy efﬁciency. A

new power-aware scheme is proposed, which reduces the total energy consumption, while maintaining a low

blocking probability under dynamic trafﬁc. Extensive simulation results are presented, which indicate that

the proposed heuristic algorithm achieves a power saving of up to 9%, compared to a simple energy unaware

dynamic RSA algorithm.

1 INTRODUCTION

The demand for bandwidth is annually growing ex-

ponentially, driven by an increasing number of global

internet users. In addition, future needs will also be

driven by emerging capacity-demanding applications,

including autonomous vehicles, the internet of things,

high bandwidth enhanced video and virtual reality.

According to Cisco, global IP trafﬁc stood at 122 Ex-

abytes in 2017 and it is estimated that these numbers

will triple by 2022 (Cisco, 2019).

Historically, WDM optical networks technologies

have used a ﬁxed grid plan to accommodate the re-

quested trafﬁc demand. Wavelengths of line rate 2.5,

10, 40, and 100 Gb/s have all been suited with 50-

GHz spacing in backbone networks and 100-GHz

spacing in metro-core networks (Simmons, 2014).

https://orcid.org/0000-0002-1895-094X

https://orcid.org/0000-0002-7058-3147

https://orcid.org/0000-0002-5771-1511

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

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

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

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

However, it is likely that bit rates greater than 100

Gb/s will not ﬁt into this scheme (Gerstel et al.,

2012). Elastic optical networks (EON), as a novel

concept of WDM networks, are considered the most

suitable architecture for backbone and next gen-

eration metropolitan networks as they are charac-

terized by high spectral efﬁciency and adaptability

(Jinno, 2017). EONs, based on orthogonal frequency-

division multiplexing (OFDM) (Dao et al., 2018) sup-

port lightpaths with different bitrates, exploit the ﬂex-

ible grid technology where the spectrum is split into

25, 12.5 GHz or less slots compared to coarser split-

ting of 50 GHz or 100 GHz of traditional WDM net-

works. Hence, the slots are combined to create chan-

nels, which are not overlapping due to OFDM’s or-

thogonality capacity, of the desired size using band-

width what is strictly necessary for the transmission

spectrum (Soumplis, 2017).

The energy consumed by ICT (Information and

Communication Technology) equipment, which is

rapidly expanding (Belkhir and Elmeligi, 2018),

(Beletsioti et al., 2016), causes a signiﬁcant economic

and environmental problem. According to European

Framework Initiative for Energy and Environmental

Beletsioti, G., Mavridopoulos, S., Tziroglou, G., Kyriakopoulos, C., Papadimitriou, G., Nicopolitidis, P. and Varvarigos, E.

Power-aware Algorithms for Energy-efﬁcient Elastic Optical Backbone and Metro Networks.

DOI: 10.5220/0007925000630070

In Proceedings of the 16th International Joint Conference on e-Business and Telecommunications (ICETE 2019), pages 63-70

ISBN: 978-989-758-378-0

Efﬁciency in the ICT Sector, ICTs account for 8-10%

of the European electricity consumption and up to 4%

of its carbon emissions. Furthermore, the network in-

frastructure is becoming a large portion of the energy

footprint in ICT. In (Pickavet et al., 2008) M. Pick-

avet et al., mention that for network equipment, the

energy consumption growth rates are typically about

12% per year. Thus, the concept of energy efﬁcient

or green networking has been emerged as a research

topic.

A multitude of published papers have considered

energy efﬁciency in the design of IP Over WDM op-

tical networks (Shen and Tucker, 2009), (Chabarek

et al., 2008), (Melidis et al., 2019). An important ap-

proach towards energy saving in IP over optical net-

works is the selective switching off of inactive net-

work components when the trafﬁc load is low, i.e.

during night hours (off-peak hours), while maintain-

ing the vital functions of the network, accommodating

the residual capacity. In (Chiaraviglio et al., 2009b),

energy efﬁcient solutions ranging from Mixed Inte-

ger Linear Programming (MILP) to heuristics are pro-

posed. More precisely, these schemes disable differ-

ent network elements when the load is reduced, while

ensuring a set of important constraints such as full

connectivity and maximum utilization of a link. The

same authors in (Chiaraviglio et al., 2009a) evaluate

the actual power consumption savings considering a

real IP backbone network and a real trafﬁc proﬁle.

Besides the selective disconnection and low power

mode of networks elements, a considerable part of

studies are focused on virtual topology reconﬁgura-

tion algorithms. The authors in (Genc¸ata and Mukher-

jee, 2003) and (Yayimli and Cavdar, 2012) propose a

method of conﬁguring the virtual topology in WDM

networks which constantly presents alternating trafﬁc

over time. Two thresholds corresponding to the load

of the optical paths are introduced: one to detect over-

loaded and one to detect underutilized lightpaths.

Additionally, various power-efﬁcient algorithms

considering the design of IP over EON (Zhu et al.,

2019) can be found in the literature. A fairly common,

yet effective method of energy saving is the exten-

sive application of optical bypass, reducing thus the

number of high energy-consuming optical-electrical-

optical (O-E-O) conversions, as the signal can be

transported, ampliﬁed and switched directly in the op-

tical domain. In (Zhang et al., 2015), energy efﬁcient

trafﬁc grooming in IP-over-elastic optical networks

taking into account sliceable optical transponders is

studied. MILP models among their corresponding

heuristics are implemented, for each of three differ-

ent types of bandwidth variable transponders, and in-

vestigated in terms of energy efﬁciency. Based on

trafﬁc and optical grooming methods, Selene heuris-

tic (Kyriakopoulos et al., 2018a) is an online algo-

rithm which exploits the innovative Signal Overlap

technique for power savings in EONs. The work in

(Vizca

ıno et al., 2012) is dedicated to the study of en-

ergy efﬁciency in optical transport networks, compar-

ing the performance of an innovative ﬂexible network

grid based on Orthogonal Frequency Division Mul-

tiplexing (OFDM) with that of Wavelength Division

Multiplexing (WDM) with a Single Line Rate (SLR)

and a Mixed Line Rate (MLR) operation. Energy-

aware heuristic algorithms are proposed for resource

allocation both in static (ofﬂine) and dynamic (on-

line) scenarios with time-varying demands for the

Elastic-bandwidth OFDM-based network and WDM

networks (with SLR and MLR). Lopez et al. in (Viz-

caino et al., 2012), provides an in depth energy efﬁ-

cient comparison between conventional path protec-

tion schemes for ﬁxed-grid (WDM) and ﬂexible-grid

(EON) networks.

In this paper, an algorithm, namely SOLA (Switch

Off Links Algorithm), that disables EON network

links during the operation phase in low-use scenarios

based on a threshold value is proposed. The proposed

technique is based on prior knowledge in techniques

which switch off and/or put in low power mode net-

work equipment in ﬁxed grid networks. However, the

impact of the contiguity constraint has not been exten-

sively researched in cases of shutting down network

components, which is the main contribution of this

work. Extensive simulation results indicate that the

presented algorithm accomplishes energy efﬁciency

and maintains tolerant bandwidth blocking probabil-

ity.

The rest of the paper is organized as follows. Sec-

tion 2 introduces the Elastic Optical Network model,

power model and constraints. Section 3 presents the

proposed algorithm. Section 4 gives the network en-

vironment, and Section 5 concludes this paper.

2 NETWORK MODEL

2.1 IP-over-EON Architecture

In this paper, a mesh based metropolitan or a back-

bone network which uses an IP-over-EON architec-

ture as shown in Fig.1, is considered. A typical EON

architecture consists of optical ﬁber links and optical

switches. Each optical switching node is connected

to the IP router ports through bandwidth variable

transponders (BVTs) (Yi and Ramamurthy, 2016).

At the starting point of the data transmission path,

the transponder, converts the electrical ﬂows com-

DCNET 2019 - 10th International Conference on Data Communication Networking

Optical switching node

Line ampliﬁer

IP routers

IP Layer

Optical Layer

Figure 1: IP-Over-EON Architecture.

ing from the IP source router to the optical domain

(E/O conversion), and then the trafﬁc entering the op-

tical layer is routed over the optical network in all-

optical connections (lightpaths). The trafﬁc travels

along the lightpath in the optical layer, arrives at the

optical layer destination node and ﬁnally reaches the

end point at the IP layer. At the destination of a light-

path, the signal is converted back to electrical at the

transponder (O/E conversion). Data is then forwarded

and handled by the corresponding IP router. In or-

der for the optical signal to travel over long distances,

line ampliﬁers are deployed along the ﬁber in every

80 km.

2.2 Power Consumption Model

The main components that may affect the energy con-

sumption of an elastic optical network are the IP

router ports, bandwidth variable transponders (BVTs)

and line ampliﬁers. Details are given below.

IP Router Ports: A 400 Gb/s IP router port is con-

nected to a bandwidth variable transponder and con-

sumes 560 Watt (Vizcaino et al., 2012), (Zhang et al.,

2015), (Biswas and Adhya, 2019) as shown in (1).

= 560(Watt) (1)

Bandwidth Variable Transponder: According to

(Zhang et al., 2015) and (Kyriakopoulos et al., 2018b)

the power consumption of a BVT can be expressed as

in (2), in which TR represents the transmission rate of

the optical transponder. An additional 20% of power

consumption is considered as an overhead contribu-

tion for each transponder.

BV T

= 1.683 × TR(Gb/s) + 91.333(Watt) (2)

Line Ampliﬁer: Erbium Doped Fiber Ampliﬁers are

considered as line ampliﬁers in this study. The power

of the EDFA is represented in Equation (3), in which

X is the spectrum width for amplifying.

EDFA

= 0.0075 × X(GHz) (3)

2.3 Elastic Optical Network Constraints

When designing an IP over EON, one should select

the route, and spectral resources for a connection re-

quest arriving to the network. This is known as the

problem of Routing and Spectrum Assignment (RSA)

(Mart

ınez and Pinto-Roa, 2017), (Fan et al., 2015).

The EON implementation imposes to the RSA prob-

lem three constraints: (1) the wavelength continuity

constraint, that is the allocation of a connection, must

follow the same wavelength on each link along the

route, (2) the spectrum contiguity constraint, that is

the allocation of a connection must be on contiguous

FS on each link along the route, and (3) the spectral

conﬂict constraint, that is a connection allocated to

a certain spectral resource, cannot overlap with the

spectral resources of other connections.

3 SOLA DESCRIPTION

The main idea of the proposed algorithm is the design

of an energy efﬁcient scheme which reduces the to-

tal energy consumption during network’s operation,

by selectively switching off networks links in low-

use scenarios while keeping the blocking probability

in low percentages under dynamic trafﬁc. In the op-

eration phase of the network, new and variable rate

connection requests arrive dynamically and have to

be served upon their arrival, one by one. SOLA algo-

rithm consists of two separate periods. The ﬁrst pe-

riod involves the observation period of the algorithm,

during which some calculations are made regarding

the utilization of the links, while the second period

refers to the estimation of total power consumption.

Algorithm 1 shows the pseudocode of the pro-

posed algorithm SOLA. During the observation pe-

riod, the algorithms starts routing the trafﬁc demands

which arrive dynamically in the network. SOLA cal-

culates the shortest paths between the node pairs, us-

ing the k-shortest path method, and routes the de-

mands according to the First Fit algorithm. During

this period, the existing links on the physical topol-

ogy are monitored for a ﬁxed number of arrivals.

By the end of the observation phase, link utiliza-

tion percentages for each link in the physical topology

have been calculated. Afterwards, links of the physi-

cal topology are sorted in descending order according

to these previously calculated values. Low threshold

value (LT) is estimated using Equation (4) and (5),

and the number of links to be switched off from the

physical topology can been determined. In detail, Eq.

(4), is an empirical formula and refers to the utiliza-

tion factor of the link. For a given network

Power-aware Algorithms for Energy-efﬁcient Elastic Optical Backbone and Metro Networks

Algorithm 1: SOLA.

Input : G(N,L): Physical Topology

N: Set of nodes in the network

L: Set of links in the network

Observation Period

Calculate k-shortest paths

Route demands FirstFit

Record link utilization statistics

Record blocked connection requests (with

respect to bandwidth)

Decision Making

According to recorded statistics do:

Order links according to utilization

Calculate UF

Calculate LT

Estimate BBP

Calculate energy consumption using

eq. 1, eq. 2 and eq. 3

foreach link ∈ L do

if link utilization <= low threshold (LT)

then

remove link from network topology

else

continue

Operation Period

Input : G

(N,L

): New Physical Topology

N: Set of nodes in the network

: Set of links in the network

Calculate k-shortest paths

Route demands FirstFit

Estimate BBP

Calculate energy consumption using

eq. 1, eq. 2 and eq. 3

load, topologies consisting of large number of links

are expected to experience lower utilization per link in

contrast to smaller ones. Therefore, it is expected that

the utilization factor (UF) is inversely proportional to

the number of active links (n) found on the physi-

cal topology. The UF formula presented in Eq. (4)

is, consequently, structured to reﬂect the aforemen-

tioned observation. Eq. (5) declares the Low Thresh-

old value. Every link that has a utilization value less

that LT should be switched off. In the ﬁnal step of this

period, the power consumption as well as the band-

width blocking probability (BBP) of the initial phys-

ical topology are estimated, and the speciﬁc links are

switched off. The number of links that has to be de-

activated is used as an input parameter to the second

phase of the algorithm. The extreme case that all links

that traverse a network node were deactivated simul-

taneously is not assumed in this study, as the network

node is isolated and the network becomes connection-

less.

Next the algorithm enters the operation phase.

During this period, SOLA, takes place on a new net-

work topology, having excluded the deactivated links.

K-shortest paths are recalculated for the new network

topology and new demands are routed using First Fit

once again. Finally, the energy consumption of the

network equipment on the new network topology and

the BBP are estimated.

UF = e

/n (4)

LT = 10 ×U F (5)

4 PERFORMANCE EVALUATION

4.1 Study Cases

In order to evaluate the performance of the proposed

algorithm, a set of simulation experiments were con-

ducted. To estimate the overall power consumption

of different design solutions, two network topologies

were considered. Fig.2 and Fig.3 show a mesh based

network (Antoniades et al., 2004), which consists of

29 nodes and 41 links, for each of which two direc-

tions will be considered and a backbone transport net-

work, NFSNET (Shen and Tucker, 2009) of 14 nodes

and 21 links, respectively.

4.2 Simulation Assumptions and

Parameters

An elastic optical network simulator has been imple-

mented, using Python 3.7 on Spyder (The Scientiﬁc

Python Development Environment). The complete set

of parameters used in this study are listed below.

1. The number of frequency slots (FS) on a link

equals to 160. This is a typical value which is

used in many previous works.

2. Each FS is assumed to have a spectral width of 25

GHz.

3. Connection requests follow a Poisson process

with an average connection’s inter arrival time

DCNET 2019 - 10th International Conference on Data Communication Networking

Figure 2: Mesh based Metropolitan network.

Figure 3: Transport backbone network, NSFnet.

(IAT ) equals to 1 (λ), while their holding time

follows a negative exponential distribution with

mean value (µ). The latter is tuned to achieve the

desired trafﬁc load (Comellas and Junyent, 2015).

4. The number of FSs per connection corresponds to

the uniform distribution. Each new coming con-

nection can take any value from 1 to 9 with a uni-

formly distributed probability (Comellas and Jun-

yent, 2015).

5. The source and destination nodes of a request

are randomly and independently selected from the

network topology.

6. K-shortest path, with k=3, and widely known First

Fit scheme, are used for solving the RSA problem.

7. One line ampliﬁer and eight BVT per physical

connection are considered in this study.

8. The offered load is determined by λ / µ (Erlang).

9. One FS as a guard-band associated with each of

the connections is considered.

10. The modulation format used in every connec-

tion is assumed to be the same during the whole

simulation and the connection requires the same

amount of bandwidth as its bit rate (Fan et al.,

2015).

4.3 Numerical Results

The performance metrics such as energy consump-

tion, bandwidth blocking probability (BBP), energy

savings and average hop distance have been evaluated

in metropolitan and backbone networks.

A simple non-energy aware routing and spectrum

assignment approach, namely Elastic, has been im-

plemented. This algorithm which provides non power

awareness, just routes the incoming requests using the

ﬁrst ﬁt strategy without deactivating any network re-

sources, exploiting the advantages of EONs. Accord-

ing to equations (4) and (5), three (SOLA -3) and two

(SOLA -2) links should be activated, when SOLA is

applied in both Metropolitan and NSFnet backbone

networks respectively. Nevertheless, for the sake of

completeness, energy consumption (Fig. 4), BBP

(Fig. 5), energy savings (Fig. 6) and average hop dis-

tance (Fig. 7), are depicted even when fewer links are

deactivated for each network topology, i.e. SOLA-1

and SOLA-2 for metropolitan network and SOLA-1

for the backbone network.

Figure 4 illustrates the total energy consumption

versus the offered load (Erlang) between SOLA and

Elastic case algorithm. In detail, Fig. 4a relates to

energy consumption in Metropolitan network, while

Fig. 4b refers to energy consumption in NSFnet back-

bone network. The energy consumption of each com-

pared method rises in a common way as the offered

load increases. It is worth noticing that in both net-

work topologies SOLA always outperforms the ref-

erence Elastic case algorithm. Corresponding results

obtained in terms of power savings are summarized in

Fig. 6. These results are translated into proﬁt by up

to 7% and 9% for Metropolitan and backbone NSFnet

networks respectively.

From the algorithm description, it is evident that a

small part of network’s resources is sacriﬁced in order

to achieve lower power consumption values. Notwith-

standing, extensive simulation results have proven

that this sacriﬁce is minimal in terms of energy sav-

ings attained. To support this, bandwidth blocking

probability (BBP), in linear and logarithmic (inline

plots in the Figure) scale, versus the increasing of-

fered load is depicted in Fig. 5. BBP of both algo-

rithms increases when the trafﬁc load increase. As

it could be seen, BBP remains the same as long as

the offered load is light for both SOLA and Elastic

case algorithm. Although, as expected in higher of-

fered load values the Elastic case algorithm results in

lower BBP, as the lightpaths have more chances to be

accommodated in a network with a greater number

of links. However, the proposed algorithm manages

to save important amounts of energy without signiﬁ-

Power-aware Algorithms for Energy-efﬁcient Elastic Optical Backbone and Metro Networks

(a) Energy consumption (Watt) in Metropolitan network.

(b) Energy consumption (Watt) in Backbone network

(NSFnet).

Figure 4: Energy consumption under different network

topologies.

cantly increasing the BBP, which ﬂuctuates from just

1 to 2.5 %.

The estimates for the average hop distances as

come from the Elastic case algorithm and the pro-

posed SOLA are given in Figure 7. More precisely,

this ﬁgure describes how many hops a lightpath tra-

verses on average to reach its destination. In both

subﬁgures 7a and 7b, the Elastic case algorithm shows

a minor advance (this minor advance could be trans-

lated to only 1-2.7% proﬁt), as there is no deactiva-

tion of any link and the possibilities of ﬁnding shorter

paths, in comparison to SOLA, are increased. This

small increment in terms of average hop distance is

a small but necessary price that the algorithm must

pay to achieve signiﬁcant energy savings, while keep-

ing the overall performance in high levels. In addi-

tion, this minor drawback is quiet common and is ob-

served in many previous energy efﬁcient related pa-

pers ((Beletsioti et al., 2018), (Kyriakopoulos et al.,

2018b)).

(a) Bandwidth blocking probability in Metropolitan net-

work.

(b) Bandwidth blocking probability in Backbone network

(NSFnet).

Figure 5: Bandwidth blocking probability under different

network topologies.

5 CONCLUSION

In this paper a new power aware algorithm, namely

SOLA, has been implemented for Elastic Optical Net-

works. The main objective of the proposed algorithm

is the reduction of energy consumption during the net-

work operation, by selectively deactivating network

links under low utilization scenarios, while it man-

ages to keep the bandwidth blocking probability in

low percentages. To attain a more realistic approach

towards energy savings in EONs, optical grooming is

intended to be introduced as a primary future work

goal. Furthermore, decision making strategies with

respect ro adaptivity issues during network operation

will also be reconsidered. The proposed scheme can

be the base of a new generation of energy efﬁcient

algorithms for elastic optical networks.

DCNET 2019 - 10th International Conference on Data Communication Networking

(a) Percentage of energy savings (%) in Metropolitan net-

work.

(b) Percentage of energy savings (%) in Backbone network

(NSFnet).

Figure 6: Percentage of energy savings (%) under different

network topologies.

ACKNOWLEDGEMENTS

This research has been coﬁnanced by the Euro-

pean Union and Greek national funds through the

Operational Program Competitiveness, Entrepreneur-

ship and Innovation, under the call RESEARCH-

CREATE-INNOVATE (project code:T1EDK-05061).

REFERENCES

Antoniades, N., Roudas, I., Ellinas, G., and Amin, J. (2004).

Transport metropolitan optical networking: evolving

trends in the architecture design and computer model-

ing. Journal of lightwave technology, 22(11):2653.

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

P. (2016). Energy-aware algorithms for ip over wdm

optical networks. Journal of Lightwave Technology,

34(11):2856–2866.

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

(a) Average Hop Distance in Metropolitan network.

(b) Average Hop Distance in Backbone network (NSFnet).

Figure 7: Average Hop Distance under different network

topologies.

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.

Belkhir, L. and Elmeligi, A. (2018). Assessing ict global

emissions footprint: Trends to 2040 & recommenda-

tions. Journal of Cleaner Production, 177:448–463.

Biswas, P. and Adhya, A. (2019). Energy-efﬁcient network

planning and trafﬁc provisioning in ip-over-elastic op-

tical networks. Optik, 185:1115 – 1133.

Chabarek, J., Sommers, J., Barford, P., Estan, C., Tsiang,

D., and Wright, S. (2008). Power awareness in net-

work design and routing. In IEEE INFOCOM 2008-

The 27th Conference on Computer Communications,

pages 457–465. IEEE.

Chiaraviglio, L., Mellia, M., and Neri, F. (2009a). Energy-

aware backbone networks: a case study. In 2009 IEEE

International Conference on Communications Work-

shops, pages 1–5. IEEE.

Chiaraviglio, L., Mellia, M., and Neri, F. (2009b). Reducing

power consumption in backbone networks. In ICC,

volume 9, pages 1–6.

Cisco (2019). Cisco visual networking index: Forecast and

trends, 2017 - 2022. Technical report.

Power-aware Algorithms for Energy-efﬁcient Elastic Optical Backbone and Metro Networks

Comellas, J. and Junyent, G. (2015). Improving link

spectrum utilization in ﬂexgrid optical networks.

IEEE/OSA Journal of Optical Communications and

Networking, 7(7):618–627.

Dao, H., Morvan, M., and Gravey, P. (2018). An efﬁ-

cient network-side path protection scheme in ofdm-

based elastic optical networks. International Journal

of Communication Systems, 31(1):e3410.

Fan, Z., Qiu, Y., and Chan, C.-K. (2015). Dynamic multi-

path routing with trafﬁc grooming in ofdm-based elas-

tic optical path networks. Journal of Lightwave Tech-

nology, 33(1):275–281.

Genc¸ata, A. and Mukherjee, B. (2003). Virtual-topology

adaptation for wdm mesh networks under dynamic

trafﬁc. IEEE/ACM Transactions on networking,

11(2):236–247.

Gerstel, O., Jinno, M., Lord, A., and Yoo, S. B. (2012).

Elastic optical networking: A new dawn for the

optical layer? IEEE Communications Magazine,

50(2):s12–s20.

Jinno, M. (2017). Elastic optical networking: Roles and

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

Technology, 35(5):1116–1124.

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

tidis, P. (2018a). Exploiting the signal overlap tech-

nique for energy efﬁciency in elastic optical networks.

In 2018 International Conference on Computer, Infor-

mation and Telecommunication Systems (CITS), pages

1–5. IEEE.

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

tidis, P. (2018b). Towards energy efﬁciency in

virtual topology design of elastic optical networks.

International Journal of Communication Systems,

31(13):e3727.

Mart

ınez, S. F. and Pinto-Roa, D. P. (2017). Performance

evaluation of non-hitless spectrum defragmentation

algorithms in elastic optical networks. In 2017 XLIII

Latin American Computer Conference (CLEI), pages

1–8. IEEE.

Melidis, P., Nicopolitidis, P., and Papadimitriou, G. (2019).

Reserved energy-aware virtual topology management

for ip-over-wdm optical networks. Optical Switching

and Networking, 31:72–85.

Pickavet, M., Vereecken, W., Demeyer, S., Audenaert, P.,

Vermeulen, B., Develder, C., Colle, D., Dhoedt, B.,

and Demeester, P. (2008). Worldwide energy needs

for ict: The rise of power-aware networking. In 2008

2nd International Symposium on Advanced Networks

and Telecommunication Systems, pages 1–3.

Shen, G. and Tucker, R. S. (2009). Energy-minimized de-

sign for ip over wdm networks. Journal of Optical

Communications and Networking, 1(1):176–186.

Simmons, J. M. (2014). Optical network design and plan-

ning. Springer.

Soumplis, P. (2017). Routing and spectrum allocation al-

gorithms in elastic optical networks. PhD thesis.

Vizcaino, J. L., Ye, Y., L

opez, V., Jim

enez, F., Duque, R.,

and Krummrich, P. (2012). On the energy efﬁciency

of survivable optical transport networks with ﬂexible-

grid. In European Conference and Exhibition on Opti-

cal Communication, pages P5–05. Optical Society of

America.

Vizca

ıno, J. L., Ye, Y., and Monroy, I. T. (2012). Energy ef-

ﬁciency analysis for ﬂexible-grid ofdm-based optical

networks. Computer Networks, 56(10):2400–2419.

Yayimli, A. and Cavdar, C. (2012). Energy-aware virtual

topology reconﬁguration under dynamic trafﬁc. In

2012 14th International Conference on Transparent

Optical Networks (ICTON), pages 1–4. IEEE.

Yi, P. and Ramamurthy, B. (2016). Provisioning virtualized

cloud services in ip/mpls-over-eon networks. Pho-

tonic Network Communications, 31(3):418–431.

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

Mukherjee, B. (2015). Energy-efﬁcient trafﬁc groom-

ing in sliceable-transponder-equipped ip-over-elastic

optical networks. IEEE/OSA Journal of Optical Com-

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

Zhu, M., Sun, Q., Zhang, S., Gao, P., Chen, B., and Gu,

J. (2019). Energy-aware virtual optical network em-

bedding in sliceable-transponder-enabled elastic opti-

cal networks. IEEE Access, 7:41897–41912.

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