Cognitive Heterogeneous Optical Networks: Benefits - ijcce

Abstract—This paper provides a comprehensive review of the

cognitive optical networks as the complexity of optical networks

has been increasing continuously in recent years. Based on our

previous studies, we propose a novel framework of

heterogeneous optical networks and introduce cognition to the

optical networks in order to improve the overall network

performance. Furthermore, this paper provides new insights on

orthogonal frequency division multiplexing (OFDM)

transmission technology, which is considered as a promising

technology for future high speed optical transmission.

Specifically, the solution to routing and spectrum allocation

(RSA) problem in cognitive elastic OFDM-based optical

networks is discussed in this paper. We also present possible

challenges and propose new ideas to guide the future work of

researchers in next generation optical networks in order to

make future optical networks more cognitive and

reconfigurable.

Index Terms—Cognitive optical networks, heterogeneous,

orthogonal frequency division multiplexing, routing and

spectrum allocation.

I. INTRODUCTION

With the development of cognitive radio techniques,

wireless networks have been gradually becoming more

intelligent as they have the ability to sense current states of

the network dynamically, self-adapting learn from history

and make intelligent decisions to adjust its transmission

parameters to ensure networks' function and users' needs.

Although cognitive radio techniques can only be used in

wireless networks, inspired by it, we are thinking about

whether it is possible to introduce cognitive function into the

intelligent control plane of optical networks and relevant

network devices to improve the performance of optical

networks.

Traditional optical networks don’t know their current

states and can not be aware of changes in the environment

when problems have occurred. Also, they have no

intelligence which acts like human nervous system that can

learn and reason for actions. Therefore, future optical

networks need to be cognitive which means they should have

a cognitive process that can perceive current network

conditions, and then plan, decide and act on those conditions.

This network can learn from these adaptations and use them

to make future decisions, all while taking into account

Manuscript received July 22, 2012; revised September 2, 2012.

The authors are with the College of Information Science and Engineering,

Northeastern University, Shenyang 110819, China (e-mail:

[email protected]; [email protected]; [email protected])

end-to-end goals [1]. Although some studies in [2]–[4] have

addressed the problem of cognitive optical networks, these

studies are somewhat preliminary and there may still be many

issues left unresolved, such as how to really realize the

cognitive process in optical networks and the consideration

of actual heterogeneous optical network structure. In our

previous works, we investigated the problems in the context

of traditional wavelength division multiplexing (WDM)

optical networks. Due to the difference of network structure

and management policy between traditional optical network

and cognitive optical network, many existing technologies

and techniques, such as routing and wavelength allocation

(RWA) methods cannot be directly used for cognitive optical

networks. Therefore, there will be great challenges and wide

innovative space for us to keep studying in cognitive optical

networks.

Recently, as the size of optical networks increases, the

optical backbone has been actually divided into multiple

domains each of which has its own network provider and

management policy. Different network provider offers

different service types, such as telephone service, TV

broadcast, video service and Internet. At the same time, with

the development of intelligent optical networks and general

multi-protocol label switching (GMPLS) technology, the

seamless converge between IP and optical networks can be

realized and the maturation of multi-layer structure for

IP/MPLS over optical networks can be accelerated.

Therefore, current optical transport networks are rather

heterogeneous which means that they are composed of

various different networking technologies and transmission

techniques, and the network management will be much more

complex. The development of heterogeneous optical

networks is the trend of next-generation optical networks and

is posing a serious challenge to existing network mechanisms.

In order to solve the growing complexity of optical networks,

we bring cognition to heterogeneous optical networks and the

research work will be of great practical significance.

The rest of the paper is organized as follows. In Section 2,

we provide an overview of cognitive optical networks and

propose a framework of cognitive heterogeneous optical

networks. In Section 3, we introduce routing and spectrum

allocation problem in our cognitive heterogeneous optical

networks based on OFDM and propose methods to solve it.

In Section 4, we present the possible challenges and

prospects for next generation optical networks and propose

new ideas that will guide the future work of researchers in

optical networks. Finally, Section 5 concludes this paper.

Cognitive Heterogeneous Optical Networks: Benefits,

Evolution and Future Challenges

Ying Wu, Weigang Hou, and Lei Guo

International Journal of Computer and Communication Engineering, Vol. 1, No. 4, November 2012

371

II. COGNITIVE HETEROGENEOUS OPTICAL NETWORKS

A. Cognitive Optical Networks

Several definitions of cognitive networks have been

proposed in [1], [5] and the design can be applied to any type

of network, being wired or wireless. Based on these

definitions, we give the following definition for a cognitive

optical network: a cognitive optical network should be

self-aware that it can observe current optical network

conditions and be aware of needs of different users, and it has

the ability of cognition that can respond and dynamically

adapt to changes of the elements in the network by acquired

knowledge through learning. It needs to consider the

network’s end-to-end performance, support trade-offs

between multiple goals and make collective decision of the

whole network to optimize overall network performance.

Furthermore, the cognitive optical networks need to evolve

over time that their technologies will be updated by removing

deprecated and adding new ones. The cross-layer design that

allows direct communication between non-adjacent layers or

sharing of internal information between layers [6] is also

required to be considered in cognitive optical networks.

In order to further understand the concept of cognitive

optical networks, we consider constructing a simple layered

architecture to make it clear. Inspired from the concept of

three-level cognition model [7] and three-layer cognitive

framework [8] of cognitive networks, we draw the

three-plane architecture of cognitive optical networks as

illustrated in Fig. 1.

Requirements Plane

(bandwidth/data-rate, transmission

distance, quality of service, etc. )

Cognitive Control Plane

(transmission technology, routing and

resource assignment scheme,

survivability strategy, etc. )

Self-aware Optical Plane

(resource availability, blocking rate,

etc. )

…

…

End to End Goals

Cognitive Process

Software Self-

aware Optical

Networks

Fig. 1. A three-plane architecture of cognitive optical networks..

The requirements plane deals with service and traffic

demands of applications or users, such as requirements on

bandwidth and quality of service (QoS), and sends them to

the cognitive control plane. Then the control decisions, such

as transmission technology, routing and resource assignment

scheme, will be made by the cognitive control plane based on

some information from observation of current conditions and

changes of network elements that obtained at the self-aware

optical plane. Furthermore, the control plane needs to

maintain a knowledge base that stores previous decision

making experiences through which the system can learn from

past decisions and make future plan. And this knowledge

base can evolve by itself over time. There are one or more

cognitive elements in the cognitive process and we consider

achieving cognition capability by machine learning and

pattern recognition methods which can improve its

performance through experience gained over a period of time

without complete information about the environment in

which it operates.

Inspired from the cognitive cycle in [9], the cognitive

ability can be realized by cognitive process module which

mainly involves the design of the cognitive cycle. The

cognitive cycle should be based on the concept of

observe-plan-decide-act loop, augmented by learn and

following end-to-end goals to achieve cognition [10]. The

self-aware network will employ sensors to observe the

environment. The observations captured by the sensors will

be further used for planning and they will also be fed to the

learning module so that the system can learn and remember

useful observations, which will aid the decision making

module in the future. The planning module determines

potential actions, such as RSA strategies to be followed based

on observations. The decision module decides on the actions

to be taken based on possible moves and experiences. Then

the acting module is responsible for the execution of actions

and adequate changes which will make the network more

reconfigurable. Taking the RSA problem for example, a RSA

cognitive cycle is required to be constructed, as shown in Fig.

2. Based on connection requirements and observation results

of the network, the appropriate RSA choice will be made

automatically. However, in traditional WDM optical

networks, changing the RSA scheme requires the use of

different transponders. There may be many cognitive cycles

in a cognitive optical network each of which has its own

function. Furthermore, the principle of constructing the

cognitive cycle is to achieve the compromise between the

information exchange overhead and cognitive ability.

RSA Act

RSA Decide

RSA Learn

RSA Observe

RSA Plan

Fig. 2. RSA cognitive cycle structure.

The self-aware optical plane acts as the physical layer of

the network and has the ability to be aware of the current

conditions and changes of network elements as well as the

forthcoming network status such as resource availability and

blocking rate. Furthermore, different planes, adjacent or

non-adjacent, can share internal information which performs

cross-layer design and the decision needs to take all the

network elements involved into account to achieve

end-to-end goals.

B. A Framework of Cognitive Heterogeneous Optical

Networks

As shown in Fig. 3, a cognitive heterogeneous optical

network is composed of several independent domains, which

are connected by inter-domain links, and each domain

corresponds to an independent cognitive subnet which is split

into two layers: IP/MPLS layer and optical layer. There are

cognitive label switching routers (LSRs) and cognitive

optical cross connections (OXCs) or reconfigurable optical

add-drop multiplexers (ROADMs) in IP/MPLS layer and

optical layer respectively.


372

Domain 2 Domain 3

Cognitive LSR

IP link

IP/MPLS Layer

Optical Layer

Fiber

Cognitive OXC/ROADM

Cognitive LSR

IP link

IP/MPLS Layer

Optical Layer

Fiber

Cognitive OXC/ROADM

Cognitive LSR

IP link

IP/MPLS Layer

Optical Layer

Fiber

Cognitive OXC/ROADM

Domain 1

Lightpath

Lightpath

Lightpath

Cognitive

Process

Cognitive

ProcessCognitive

Process

Fig. 3. A framework of cognitive heterogeneous optical networks.

Fig. 4. A cognitive optical node architecture

Fig. 4 shows a specific cognitive optical node architecture

in our proposed cognitive optical network. At the node level,

novel bandwidth variable (BV) transponders and bandwidth

variable OXCs/ROADMs need to be developed. The BV

transponder can be realized by using OFDM technique as the

sub-wavelength services can be provided by adjustment of

the number of OFDM sub-carriers in the transponder and for

super-wavelength services several OFDM channels can be

merged together into a super-channel [11]. The BV OFDM

transponder generates an optical signal in the electrical

domain using just enough spectral resources in terms of

sub-carriers with appropriate modulation level according to

the transmission rate, transmission distance or specific

channel conditions in order to achieve higher spectrum

utilization in contrast to WDM rigid fixed-grid wavelength

allocation. In [12], a BV OFDM transponder, which supports

bit rates from 40 to 440Gb/s, with 10Gb/s granularity and

10Gb/s sub-carrier spacing, was experimentally

demonstrated. Meanwhile, the BV OFDM OXCs/ROADMs

will also be used to create a connection with sufficient

spectrum to establish an end-to-end optical path in the

dynamic network environment. At the network level, RSA

algorithms are required to be studied as the RWA algorithms

of traditional WDM networks are no longer applicable here.

III. COGNITIVE ROUTING AND SPECTRUM ALLOCATION

TECHNIQUE IN ELASTIC OFDM-BASED OPTICAL NETWORKS

A. RSA Technique in OFDM-Based Optical Networks

Recently, OFDM has been proposed as a promising

modulation technique in future high-speed optical network

for its high spectrum efficiency, flexibility and robustness

against inter-carrier and inter-symbol interference. As a

special class of multi-carrier modulation schemes, OFDM

transmits a high-speed data stream by dividing it into a

number of orthogonal channels, referred to as sub-carriers,

each of which carrying a relatively low data rate [13].

Therefore, OFDM-based optical networks can provide high

spectrum utilization by flexible spectrum allocation that

supports sub-wavelength, super-wavelength and

multiple-rate data traffic accommodation. It has been verified

that the spectrum utilization improves by 5-95% compared to

traditional WDM networks and the precise improvement

depends on the network topology and traffic pattern [14].

A heterogeneous optical network based on cognition has

been shown in Fig. 2 and the corresponding simplified

intra-domain node architecture consisting of 5 nodes and 6

links is shown in Fig. 5(a). For simplicity, we focus on the

study of the RSA problem in the context of single domain and

it can be expanded to multiple domain circumstance. In Fig.

5(a), we assume the entire spectrum width of each optical

link corresponds to 10 slots. A spectrum path SP1 of 3

sub-carriers from 1 to 2 and a spectrum path SP2 of 2

sub-carriers from 1 to 3 are established according to dynamic

traffic demands. In the following discussions, we assume that

the guard carrier consists of GC sub-carriers and GC=1.

In elastic OFDM-based optical networks, a fundamental

problem is to route and allocate spectrum resources to

accommodate traffic demands, which is defined as the RSA

problem [15], and this is more challenging than traditional

RWA problem as it has some constraints. First, a connection

(spectrum path) requiring a certain capacity which is larger

than that of an OFDM sub-carrier should be assigned a


373

number of contiguous sub-carrier slots, which is known as

sub-carrier consecutiveness constraint. Second, the spectrum

path should use the same spectrums along its routing path and

this is called the spectrum continuity constraint. Third, the

guard carrier constraint requires that when two spectrum

paths are overlapping in their routing path, the corresponding

allocated spectrum slots need being separated by the

spectrum guard band which normally occupies an integral

number of sub-carrier spectrum widths [11]. Furthermore,

the sub-carrier capacity constraint guarantees that one

sub-carrier can only be used for satisfying one spectrum path

[16].

a) b)

2

451

3

1

5 4

3

2

6

SP

1

SP 2

SP1 SP1 SP1 GC SP2 SP2

1 2 3 4 5 6 7 8 9 10

Sub-carrier index

Opti

cal

Lin

k

1

4

2

3

1 2 3 4 5

5

6

6

7 8 9 10

Sub-carrier index

c)

sp1

sp2

Fig. 5. An example of RSA in OFDM-based optical networks: (a) Network topology; (b) Spectrum allocation on optical link 1; (c) Spectrum allocation on each

optical link in the network

The aforementioned constraints complicate the RSA

problem solving and one example of the corresponding RSA

in our network is shown in Fig. 5(b). In Fig. 5(b), each

sub-carrier on the fiber has an index. The sub-carriers with

index 1, 2 and 3 are assigned to SP1 which requires 3

consecutive sub-carriers. The sub-carriers with index 5 and 6

are assigned to SP2 which requires 2 consecutive sub-carriers.

The sub-carrier with index 4 is assigned as the guard carrier

between SP1 and SP2 since they are overlapping on optical

link1. Furthermore, enlightened by the grid presentation of

the overall spectrum resources in [17], the utilization of each

frequency slot in our network can be represented as shown in

Fig. 5(c), where a number of paths (sp1, sp2) have been

already established according to aforementioned dynamic

traffic demands. We assume that the connection requests

dynamically arrive and hold for a period and then leave.

When they leave, the corresponding occupied spectrum

resources will be released for other connection requests.

B. Cognitive RSA Technique

In our cognitive OFDM-based heterogeneous optical

networks, we focus on the study of RSA problem based on

cognition technique with the aim to achieve high spectrum

utilization and low blocking probability. The

distance-cognitive spectrum resource allocation concept can

be introduced to the network based on cognitive modulation

scheme and novel bandwidth variable transponders.

According to the transmission distance of the optical path,

appropriate modulation level with acceptable quality of

transmission and just enough spectrum resources will be

selected by cognition methods. The transmission over longer

distance paths can select lower modulation level with wider

spectrum and vise versa. For example, for the same data rate,

QPSK carries twice the number of bits per symbol of BPSK,

and consequently requires half the spectrum bandwidth,

while its OSNR tolerance is lower than BPSK, meaning

shorter distance reach. Therefore, the spectrum resources can

be saved by reducing the symbol rate and increasing the

number of bits per symbol, and spectrum cognition based on

the modulation type awareness will greatly improve the

spectrum resource utilization.

The service-cognitive spectrum allocation scheme is

another cognitive RSA technique. The main idea of this

concept is to be able to specify different type of business

requirements which will be then translated into a form that

can be used to configure network resources automatically. In

heterogeneous optical networks, there are numerous types of

services offered by different network service providers and

different services have quite different preferences, such as

different requests for bandwidth and QoS. The cognitive

RSA technique is capable of accommodating appropriate

route and spectrum resources which is most suitable to each

service request. When the network environment changes, the

cognitive plane should be able to sense that and act in such a

way to comply with the requests.

The RSA cognitive process contains two control loops,

one for maintaining the current states and the other for

network reconfigurations. During the training stage, the RSA

solutions have been stored in the knowledge base for future

use. When the connection requests come, the cognition

process will be triggered in real time. The most appropriate

RSA solution will be selected according to the connection

requirements and observation results of the entire network

environment, and this will be evaluated in terms of some

parameters such as spectrum efficiency, blocking rate, etc. If

the solution is fit for the current situation (the parameters are

satisfying), it will be adopted, otherwise, the system will

make analysis and learn new solutions that best fit current

traffic request, and update the knowledge base for future use

so the system can evolve continuously. For example, when

the network traffic load is increasing more than the threshold,

this will be observed by the self-aware network so that the

more suitable RSA algorithm will be adopted by the

cognitive process. On the other hand, in a dynamic traffic

environment, large connection setup and release may lead to

spectrum fragmentation throughout the network, which may

separate the available spectrum into small non-contiguous

spectrum bands, and will result in insufficient contiguous

spectrum. Furthermore, when the network operates for a

certain long time, the allocated optical routes and spectrum

may not be optimal. These problems will increase the

blocking probability of incoming connection requests and

reduce spectrum efficiency. Therefore, the network needs to

be reconfigured to reallocate the routes and spectrum

resources according to the current observation results of

network environment changes. In addition, the cognition

mechanism should be a multi-objective optimization

algorithm in order to improve the performance of the whole

network.


374

IV. PROSPECTS

With the rapid development of optical networks, the

architecture for the future optical networks has becoming

more complex than ever before and this poses new challenges

for future optical system design. We can prospect that the

future way on network control mechanisms for new

generation optical networks will trend to bring intelligence to

the network. Based on our previous work on heterogeneous

optical networks, we will apply cognition to the network and

discuss in particular the techniques that will enable the

optical network to observe, act, learn and optimizes its

performance. Furthermore, the research on this novel optical

network architecture based on OFDM as well as the key

enabling technologies will be significant and promising for

next generation optical network development.

V. CONCLUSIONS AND FUTURE WORK

This paper has comprehensively reviewed the existing

researches of cognitive optical networks and has analyzed the

shortages of current studies. Based on our previous studies on

heterogeneous optical networks, this paper has proposed a

novel framework of cognitive heterogeneous optical

networks and specifically proposed solutions to routing and

spectrum allocation problem in cognitive optical networks,

which can well guide the future work of researches in

cognitive optical networks. In this novel heterogeneous

OFDM-based optical network, there are many remaining

issues, especially on dynamic routing and spectrum

allocation, survivability strategies, traffic grooming, energy

savings, etc. The research area is underexploited in these

respects and more research is needed to realize the full

potential of this novel network architecture.

ACKNOWLEDGMENT

This work was supported in part by the National Natural

Science Foundation of China (61172051, 61071124), the Fok

Ying Tung Education Foundation (121065), the Program for

New Century Excellent Talents in University (11-0075), the

Fundamental Research Funds for the Central Universities

(N110204001, N110604008), and the Specialized Research

Fund for the Doctoral Program of Higher Education

(20110042110023, 20110042120035)

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Ying Wu received the M.S. degrees in communication

and information systems from Northeastern University,

Shenyang, China, in 2006. She is currently pursuing the

Ph.D. degree in the same university. Her research

interests include cognitive optical networks, routing and

survivability.

Weigang Hou received the M.S. degrees in

communication and information systems from

Northeastern University, Shenyang, China, in 2009. He

is currently pursuing the Ph.D. degree in the same

university. His research interests include green optical

networks and network virtualization.

Lei Guo received the Ph.D. degree in communication

and information systems from University of Electronic

Science and Technology of China, Chengdu, China, in

2006. He is currently a professor in Northeastern

University, Shenyang, China. His research interests

include network survivability, optical networks, and

wireless multi-hop networks. He has published over 100

technical papers in the above areas. Dr. Guo is a member

of IEEE and OSA.


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Cognitive Heterogeneous Optical Networks: Benefits - ijcce