cognitive heterogeneous optical networks: benefits - ijcce
TRANSCRIPT
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.
International Journal of Computer and Communication Engineering, Vol. 1, No. 4, November 2012
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
International Journal of Computer and Communication Engineering, Vol. 1, No. 4, November 2012
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.
International Journal of Computer and Communication Engineering, Vol. 1, No. 4, November 2012
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.
International Journal of Computer and Communication Engineering, Vol. 1, No. 4, November 2012
375