Internet DRAFT - draft-siracusa-nmrg-ccon-fwk
draft-siracusa-nmrg-ccon-fwk
Internet Research Task Force D. Siracusa
Internet Draft A. Francescon
Intended status: Informational E. Salvadori
Expires: May 2014 CREATE-NET
R.J. Duran
I. de Miguel
R.M. Lorenzo
Universidad de Valladolid
November 4, 2013
Framework for Cognitive Capable Optical Networks
draft-siracusa-nmrg-ccon-fwk-00.txt
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Abstract
The increased complexity in the management of highly heterogeneous
optical networks is recently forcing vendors and providers to look
for novel mechanisms which diminish the manual intervention by
favoring the autonomous execution of several operational tasks,
especially when dealing with network congestion or failure events.
The adoption of cognitive techniques in networking envisions a
network which is able to adapt itself to current or forecasted
conditions by taking into account previous history, and which is
able to act proactively, rather than reactively, in order to avoid
problems before they arise. In this document, a novel architectural
framework that introduces cognitive techniques in the optical
networking domain is described, and several use cases provided to
emphasize its effectiveness.
Table of Contents
1. Introduction ................................................. 3
2. Background ................................................... 5
2.1. Software-defined adaptable elements ..................... 5
2.2. Monitoring elements...................................... 6
2.3. Control system running cognitive processes .............. 8
3. Framework .................................................... 9
3.1. The Cognitive Decision System .......................... 11
3.2. Processes and knowledge bases .......................... 13
4. CCON Use Cases .............................................. 14
4.1. Quality of Transmission assessment ..................... 14
4.2. Path Computation ....................................... 15
4.3. Virtual Topology Design and Reconfiguration ............ 16
5. Implications on the Control Plane ........................... 16
5.1. Disseminate network configuration information .......... 16
5.2. Feed the cognitive processes with network data and
statistics .................................................. 17
5.3. Implement the decisions of the cognitive processes on the
device ...................................................... 17
6. Contributing Authors ........................................ 18
7. Security Considerations ..................................... 19
8. IANA Considerations ......................................... 19
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9. References .................................................. 20
9.1. Informative References ................................. 20
10. Acknowledgments ............................................ 22
1. Introduction
Optical networks are facing increased levels of heterogeneity, from
types of services to transmission technologies. Hence, a key issue
of highly heterogeneous networks is how to efficiently control and
manage network resources while fulfilling user demands and complying
with quality of service requirements. A solution for such a scenario
may come from cognitive networks, also known as learning-capable
communication networks [Tav2011].
+----------------+ +-------------+
| Orient | | End-to-end |
| (Plan) |<---| goals |----+
| | | | |
+----------------+ +-------------+ |
^ ^ | |
| | | v
| | | +-----------+
| | | | |
| | +------------------>| Decide |
| | | |
| +---------+ +-----------+
| | ^ |
| v | |
+-------------+ +-------+ | |
| | | |<---------+ |
| Observe |--->| Learn | |
| | | |<---------+ |
+-------------+ +-------+ | |
^ | |
| | |
| | v
+-------------+ +---------+
| | | |
| Environment |<-------------------| Act |
| | | |
+-------------+ +---------+
Figure 1 The cognitive loop
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A cognitive network is defined as "A network with a process that can
perceive current network conditions, and then plan, decide, and act
on those conditions. The network can learn from these adaptations
and use them to make future decisions, all while taking into account
end-to-end goals." [Tho2006], that is, the network implements the
so-called cognitive loop (Figure 1). Hence, there are three main
ingredients in such a network:
o Monitoring elements, which provide the network with the
perception of the current condition including physical layer
status, power consumption, traffic patterns, useful to enable an
aware network.
o Software-defined adaptable elements, which provide the network
with the capacity of modifying its current configuration, thus
enabling an adaptive network.
o Cognitive processes, which learn or make use of past history, so
that even when facing two equivalent scenarios, the network (or
the entity containing those cognitive processes) may act in a
different way if its previous history is different.
Therefore, a cognitive network is a network which is able to adapt
itself to current or forecasted conditions by taking into account
previous history, and act proactively, rather than reactively, in
order to avoid problems before they arise. Moreover, those tasks
should be performed autonomously, with little or no intervention of
the network operator. Cognitive networks are thus closely related
with autonomic networks [Beh2013]. An autonomic network relies on
self-configuration, self-healing, self-optimization and self-
protection functionalities, so that it may make decisions without
manual intervention or external help (e.g., human administrator)
[Beh2013][Mov2012]. In this way, an autonomic network is not only
aware and adaptive, but also automatic. Therefore, a cognitive
network can be considered as a variant of an autonomic network
[Mov2012], but it emphasizes the self-optimization functionality as
well as the use of learning mechanisms, in contrast with other types
of autonomic networks, which generally rely on policy-based methods
rather than on learning techniques to support the adaptations
[Mov2012, table VIII].
This document partially leverages on [Tav2011], an informative
document which describes the opportunities and challenges for a
technology-independent Learning Capable Communication Network
(LCCN). Of course, given the focus on transport networks, the
document will apply the concepts envisioned in that document to the
specific optical networking domain.
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The structure of the document is the following. In Section 2 a
review of the recent enhancements of the optical and control
technologies that are enabling Cognitive Capable Optical Networks
(CCON) is provided. Section 3 describes the framework and the
related building blocks of a CCON. Section 4 focuses on a set of
applications of cognition proposed in optical networks while Section
5 describes the implications on the Control Plane.
2. Background
A novel paradigm, like the one proposed in this document, has
emerged thanks to the recent availability of novel optical and
control system technologies. The scope of this section is to review
these enabling technologies.
2.1. Software-defined adaptable elements
Software-defined adaptable elements are essential for the
realization of the cognition concept in optical networks since they
allow the optimum and on-demand use of resources, according to the
intelligent (i.e., cognitive) processing of connection demands.
Although a cognitive network could rely on a set of fixed
transceivers in the nodes, the higher degree of flexibility provided
by software-defined transmitter and receiver subsystems is turning
them into key network elements to perform the adaptable allocation
of traffic demands.
In practice, the transmitted bandwidth adaptability in optical
transceivers is realized by: a) altering the modulation level or
format (i.e., the bits per symbol) per optical carrier and b)
varying the number of electronic or optical carriers in multi-
carrier formats [Ger2012]. The general purpose of these adaptable
schemes is to apply the optimum format over the minimum number of
carriers, thus maximizing the spectral efficiency (i.e., the number
of bits/s/Hz) for a certain traffic demand over an optical path with
certain end-to-end performance requirements.
Format adaptability can be performed either in the optical domain,
by simply enabling or disabling the different arms of nested Mach
Zehnder Modulator (MZM) structures at the transmitter and the
related output port of 90 deg hybrid at the receiver, or directly in
the electronic domain by appropriately defining the signal levels of
the modulation signals. Moreover, for multi-carrier schemes based on
electronic generation of subcarriers, the subcarrier number is
defined in the electronic domain by the length of the digital signal
processing function prior to the optical modulation, while for
optically generated subcarriers, their number is defined either by
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filtering the appropriate number of carriers or by gating the
appropriate number of subcarrier transmitter outputs directly in the
optical domain.
The bandwidth adaptable data transmission schemes mentioned above
can realize the optimum use of network resources according to the
traffic demands, but they result in added complexity in terms of
control. This is attributed to the fact that any decision mechanism
must account for a large number of possible combinations (i.e.,
central wavelength allocation, format and number of subcarriers) to
optimally serve a demand for a given optical path. The role of
cognitive optical networking is particularly beneficial for the
practical implementation of these schemes, since it can
significantly relax the decision mechanism, by exploiting past
history. It is noted that cognition can apply in combination with
any adaptable (flexible) transmission technique, since all of them
are intrinsically software-defined schemes.
2.2. Monitoring elements
Both traffic and optical performance monitoring techniques are
required to know the current state of the network. That information
can be used not only for making immediate decisions but also as an
input for forecasting procedures facilitating the execution of
proactive actions. While existing techniques for traffic monitoring
- leveraging on protocols like SNMP [RFC1157], RMON [RFC4502] - can
be also exploited in cognitive optical networks, the introduction of
new optical transmission systems, and their coexistence, triggers
the need of the development of novel Optical Performance Monitoring
(OPM) techniques.
Thus, to guarantee that the Quality of Service (QoS) and resiliency
are achieved along the lightpaths, monitoring of the physical
properties of the signal is required. OPM analyzes the accumulation
of the so called "non-catastrophic" transmission impairments such as
Chromatic Dispersion (CD), Polarization Mode Dispersion (PMD) and
non-linear effects [Cha2010]. These effects, combined with the
accumulation of network physical impairments, like crosstalk,
Amplified Spontaneous Emission (ASE) noise, Polarization-Dependent
Loss (PDL) and filter/ROADM (Reconfigurable Optical Add and Drop
Multiplexer) concatenation make the information data unrecoverable
even though the received optical signal power is at an acceptable
level. Furthermore, the so called "catastrophic" impairments such as
accidental fiber cuts and damaged or improperly installed network
elements can cause critical network performance degradations.
Meanwhile, other channels co-propagating in the same link can be
affected as well due to transients in the amplifiers caused by the
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rapid change of the total optical power (several dBs). Despite the
nature of the failure, it becomes clear that an accurate and fast
parameter monitoring would allow an early fault analysis with a fast
switching to a protection path. The efficiency and reactivity to
different problematic events also depends on the critical
interaction between OPM and higher-level control and management
plane systems. Therefore, monitoring devices must be placed in
strategic places during the planning stage of an optical network.
In 10 Gb/s and 40 Gb/s optical networks, various OPM techniques have
been developed relying on external devices such as Optical Spectrum
Analyzers (OSAs), RF devices and frequency-selective polarimeters.
On the other hand, modern transmission technologies for 100 Gb/s,
400 Gb/s, 1 Tb/s and beyond are based on coherent technologies by
taking advantages of powerful and cost-effective Digital Signal-
Processing (DSP) capabilities. OPM techniques based on DSP, where
expensive external devices are not required, are adaptable to
varying data rates and modulation formats, and are capable of
realizing joint monitoring of key physical layer parameters like CD,
PMD, PDL, OSNR, Bit-Error-Rate (BER), etc. The DSP has already been
integrated in the receiver side, so it will provide network
information at the end points. Furthermore, in the future, DSP could
also be integrated in optical amplifiers or ROADMs, thus allowing
the derivation of relevant information at these mid-points.
In DSP-based OPM techniques, Frequency-Domain (FD) equalization
combined with Data-Aided (DA) channel estimation can be considered
as a promising technology. Compared with Non-Data-Aided (NDA)
methods based on gradient algorithms for Time-Domain (TD) filters,
which are strongly dependent on the modulation format and suffer
from a relatively slow convergence with potential sub-optimum
acquisition and even failures, the DA channel estimation, based on a
periodically transmitted Training Sequences (TS), allows
instantaneous filter acquisition, immediate OPM, and the modulation
format can be altered arbitrarily in between the fixed training
patterns. All these benefits come at the cost of slight bandwidth
efficiency degradation due to the insertion of TS, and the required
overhead can be below 5%. Moreover, in a coherent burst-mode
receiver, each burst must be instantaneously equalized and only DA
channel estimation is suitable.
These DSP-based OPM techniques can be implemented in hardware, and
therefore real time physical impairment information will be
available for the control plane. However, if off-line DSP processing
is used instead, then the control plane database can be periodically
updated by the OPM with the physical impairment information, and
thus the control plane does not need to wait for the DSP processing.
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2.3. Control system running cognitive processes
In a cognition-capable optical network architecture, the
coordination among the "brain" that makes decisions and establishes
network operations and the data plane (photonic layer) is provided
by a control system, which implements the mechanisms supporting the
cognitive intelligence in an automated and reconfigurable manner.
Two different approaches can be envisioned to implement a cognitive
architecture: (i) centralized, in which the network and all
components are under the control of a single cognitive entity, which
receives all the information related to network configuration,
availability, monitored parameters, etc.; and (ii) distributed, in
which there is not a specific node with a prominent role, and where
the cognition is distributed among all the network nodes (or a large
part of them), which exchange the information mentioned above. Both
the centralized and the distributed cognitive architectures need for
a system delivering updates related to network status, reserving the
resources, and configuring the optical devices. These tasks are
carried out by the Control Plane (CP).
A cognitive optical network is expected to make effective decisions
by leveraging on a knowledge base, built with the support of the CP.
Decisions are made for different activities, such as lightpath
activation in response to a user request or re-arrangement of active
network connections. In such a context, and in particular for the
latter activity, knowledge about the status of currently active
lightpaths is required. While it is evident that this information
can be disseminated by adapting already existing protocols, it is
also clear that it would demand for the exchange of a non-negligible
amount of data between distributed control nodes (including the path
of each active connection, physical layer impairments, etc.). Hence,
from the operational point of view, a solution with distributed
control entities may not be cost-effective. Furthermore, the lack of
a global view about the network status in a distributed architecture
may lead to conflicting decisions. Finally, cognitive decisions also
rely on the values collected by the monitoring system of the
network. In this case, a distributed solution is hard to be kept
updated, since the information collected by the monitors flows
through the network and is hard be processed reliably at a single
instance (i.e. as in the case of a centralized approach). On the
other side, a centralized approach may suffer of scalability issues,
and the cognitive entity is potentially a single point of failure of
the network. While the latter issue may be lessened by enhancing the
protection/robustness of the cognitive entity and by introducing
backup entities, the former is a matter of network scenarios. In the
context of optical networks and with a limited amount of managed
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nodes, a centralized approach could still scale sufficiently, while
ensuring a high level of reliability and providing more effective
path computations. This is true for the case of core networks and in
particular national backbone networks that require the employment of
bandwidth flexible resource allocation mechanisms over a well-
defined and limited number of nodes.
3. Framework
Several architectures leveraging on cognitive mechanisms have been
recently proposed in literature to determine how (and where) the
three aforementioned key ingredients - software adaptable elements,
monitoring elements, and control systems - are implemented, how they
are glued together, as well as which tasks are going to be solved
with the help of cognition. Generic cognitive architectures have
been proposed in [Tho2006], [Kli2010] and [Tav2011], while cognitive
architecture for optical networks have been proposed in [Zer2010],
[Wei2012] and [deM2013].
These architectures show that cognition can be implemented in
different dimensions, in terms of devices and protocol layers. For
instance, in a cognitive network implementation, software-defined
transceivers may include monitoring functionalities together with
internal intelligence to modify their configuration autonomously,
i.e., being truly cognitive transceivers. However, another
implementation may opt for shifting the intelligence in charge of
configuring those transceivers to the upper layers of the nodes,
where the transceivers are located, thus being the network nodes the
cognitive elements rather than the transceivers themselves. That
example may find its way in a network with distributed cognition,
where all network nodes are equipped with cognitive capabilities and
collaborate in sharing the acquired knowledge. Nevertheless, another
possibility is a network with centralized cognition, where a single
node (the control node) contains the intelligence and makes
decisions which are then communicated to the remaining network nodes
by means of control plane protocols with suitable extensions.
On the other hand, the level and type of cognition to be added to a
network depends not only on the adopted approach but also on the
capabilities of network monitors and software-adaptable elements
employed; the higher the flexibility of the available elements, the
higher the potential of cognition. However, although the utilization
of software-defined transceivers and flexible networks, as well as
software-defined networking techniques [Das2012], is usually
associated with cognitive optical networks, it should be noted that
these technologies are not strictly necessary for adopting a
cognitive networking approach.
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+-+ +------------------------------------------------------------+
| |<=>| User interface module |
| | +------------------------------------------------------------+
| | ^
| | +--------------------|-----------------------------------+
| | | | Knowledge bases |
| | +----------------------|---------------------------------+ |
| | | v Learning modules | |
| | +--------------------------------------------------------+ | |
|C| | Cognitive Decision System (Cognitive processes) | | |
|o| | +----------------------------+ | | |
|n| | | | | | |
|t| | v v | | |
|r| |+---------+ +----------+ +--------+ +-----------+ | | |
|o| || Traffic | | Virtual | | RWA/ | | QoT | | | |
|l| ||Grooming |<->| Topology |<->| RMLSA |<->| Estimator | | | |
| |<=>|| Module | | Design | | Module | | Module | | | |
|P| || | | Module | | | | | | | |
|l| |+---------+ +----------+ +--------+ +-----------+ | | |
|a| | ^ ^ ^ ^ | | |
|n| | | | | | | | |
|e| | v v v v | |-+
| | |+--------------------------------------------------+ | |
|p| || Network Planner & Decision Maker Module | |-+
|r| |+--------------------------------------------------+ |
|o| +--------------------------------------------------------+
|t| ^ ^
|o| | |
|c| v |
|o| +-----------------------------------+ |
|l| | Optical signal monitoring & | |
|s| | transponder interface | v
| | |+-----------++--------++----------+| +------------------+
| | || Software ||Optical || Digital || |Traffic monitoring|
| |<=>|| Defined || Signal || Signal || | interface |
| | ||Transponder||Monitors||Processing|| | +--------------+ |
| | |+-----------++--------++----------+| | | Traffic | |
| | | Optical network (PHY interface) | | | Monitoring | |
| | +-----------------------------------+ | | System | |
| | | +--------------+ |
| |<=======================================>| NMS |
+-+ +------------------+
Figure 2 CCON schematic architecture
The proposed framework focuses on a key building block called
Cognitive Decision System (CDS). The CDS determines how to handle
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traffic demands and network events, and optimizes network usage and
performance by taking into account both the current status of the
network and past history. The CDS also instructs the control plane
to configure network elements accordingly. Cognition can be
implemented in a either centralized or distributed ways, depending
on whether the CDS is a single instance running on a single control
node in the whole network or it is implemented in different network
nodes. Figure 2 shows the major building blocks of a centralized
architecture, where the CDS is based on a single running instance.
The CDS is assisted by a Control Plane (CP), which feeds the CDS
with updates regarding network status and resource availability,
grants the delivery of the decisions made by the CDS to all the
interested nodes, and watches over the device configuration process,
notifying any malfunctioning or anomaly.
The proposed architecture also includes software-defined adaptable
elements, which implement the decisions made by the CDS (that are in
turn communicated by the CP) and a Network Monitoring System
(NMonS), which provides traffic status and optical performance
measurements to the CDS (again, by means of the CP). The
functionalities of adaptability and monitoring are handled in each
node through a physical layer manager, which works as a common
interface toward the CP, and also through the Network Management
System (NMS).
In the following, an overview of each component of the proposed
framework is provided.
3.1. The Cognitive Decision System
The Cognitive Decision System (CDS) is involved in very diverse
tasks related to network control and optimization. Hence, rather
than implementing the whole CDS as a monolithic module, this is
divided in different modules, each offering a functionality (or a
set of related functionalities), and all of them exploiting
cognition.
Each module implements a feedback loop where interactions with the
"environment" guide current and future interactions. However, the
feedback loop should not only observe and provide decisions, but a
learning module must also be implemented so that it prevents
mistakes from previous iterations from being made on future
iterations. Each module implements the cognitive loop shown in
Figure 1. The CDS modules consist of two main parts:
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1. The Cognitive Process, which implements the algorithms to make
decisions. It takes into account the current network status and
previous history.
2. The Knowledge Engineering Subsystem, which handles the
information used by the cognitive process. This element consists
of a knowledge base and a learning module, which links the
cognitive process with its associated knowledge base and executes
methods to update the knowledge base with acquired experience.
+--------------------------------------------+
| Cognitive Decision System |
| |
|+------------------------------------------+|
|| Knowledge Engineering Subsystem ||
+----------+ Network || +-----------+ +----------+||
| Network | status || | Generic | | Specific |||
|Monitoring|---------|->| Knowledge |<---+ +--------| Knowledge|||
| System | || | Base | | | | Base |||
+----------+ || | (Network | | | | |||
OBSERVE || | status) | | | +----------+||
|| +-----------+ | | ^ ||
|| | | LEARN | ||
|+----------------------------+ v ||
| | | | +----------+||
| v v | | Specific |||
+---------+ Request | +-------------+ | | Learning |||
| Control |----------|------------>| Specific | | | Module |||
| Plane | Decision | | Cognitive | | +----------+||
|protocols|<---------|-------------| Process | +-------------+|
+---------+ | | Module | |
ACT | +-------------+ |
| ORIENT & DECIDE |
| |
+--------------------------------------------+
Figure 3 Relationship between a cognitive process and its associated
knowledge base
Figure 3 presents the building elements of a module of the CDS, as
well as their relationship to the network monitoring system and the
control and management system. The network monitoring system gathers
the network status to a generic knowledge base. Separately, there
are specific knowledge bases containing all the information
associated with each of the cognitive processes. Therefore, there
are as many specific knowledge bases as cognitive processes in the
CDS. These databases are updated through a specific learning module,
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which is associated to a single cognitive process. Consequently, a
cognitive process module can access these two databases (generic and
specific) to retrieve information and to update them. Finally, when
a decision is made to handle a request or network event, the
decision will be communicated to the control plane for its
execution.
3.2. Processes and knowledge bases
The proposed CDS consists of five cognitive processes running in
parallel, as shown in Figure 2. These processes are implemented in
the following modules:
1. Traffic Grooming (TG) Module: It is in charge of routing non-
optical traffic demands, as for example time division multiplexed
(TDM) label-switched paths (LSPs) through existing lightpaths
composing the current virtual topology.
2. Virtual Topology Design (VTD) Module: It is in charge of
(re)designing the virtual topology and hence the set of
lightpaths to be established in the network. This module is used
for optimizing network performance by rearranging existing
connections.
3. RWA/RMLSA Module: In networks following the ITU-T grid, it solves
the routing and wavelength assignment (RWA) problem as well as
determines the modulation level. In elastic networks, it solves
the routing, modulation level and spectrum allocation (RMLSA)
problem.
4. QoT Estimator Module: It provides estimation (i.e., a theoretical
prediction) about the quality of transmission (QoT) of new
lightpaths to be established in the network as well as the impact
on existing connections when undertaking a new one. Thus, the
establishment of impairment-aware optical connections relies on
this module. Once a new lightpath is established, it verifies the
real QoT (which is provided by network monitors) and uses this
information to improve the performance of the module for future
estimations.
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5. Network Planner & Decision Maker (NPDM) Module: This module
receives user requests and handles them. It is in charge of
deciding whether a traffic demand should be directly routed on
the virtual topology or a new lightpath should be established and
which parameters should employ. It also determines if the virtual
topology or the spectrum allocated to connections should be
optimized. In order to solve those tasks it coordinates the
operation of the other modules relying on their results. The
network planner communicates the actions to be performed to the
network nodes through control plane protocols and handles the
information received from the network monitoring system.
Each module has an associated Knowledge Base (KB) in the knowledge
engineering subsystem, which is linked to the cognitive process by
means of a learning module. Some of these databases can be read by
all modules, since they contain services requirements as well as
current network status. These generic databases are:
1. Global Traffic Engineering Database (GTED): contains the
information about traffic status in the network.
2. Global Physical Parameters Database (GPPD): contains the
information about the physical topology of the network, and the
physical monitoring data.
3. SLAs/QoS/QoT requirements: This database contains the service
level agreements (SLAs) QoS and QoT parameters associated with
different services. Hence, when the cognitive system receives a
request associated to a class of service, it can obtain the
values of quality of transmission that should be guaranteed when
handling that request.
4. CCON Use Cases
In this section, a number of applications of cognition proposed in
optical networks are discussed.
4.1. Quality of Transmission assessment
As described in Sect. 3.2, the establishment of impairment-aware
optical connections relies on the QoT estimator module. It should be
noticed that once a new lightpath is established, the QoT is
verified by means of network monitors, and the result of this
verification may be used to improve the behavior of the module for
future QoT estimations.
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The cognitive operation of this module relies on the utilization of
data mining techniques [Wit2011]. A cognitive QoT estimator based on
Case-Based Reasoning (CBR) has been recently proposed in [Jim2013].
The key idea in CBR is to solve a new problem by relying on previous
experiences (or cases), which are stored in a Knowledge Base (KB).
Thus, when facing a new problem, the most similar cases stored in
the KB are retrieved, and by reusing those cases, either directly or
after adapting them, a solution to the new problem is provided.
Moreover, the KB can be updated to include new experiences, which
can lead to improving the performance of the system.
4.2. Path Computation
The CDS offers the functionality of determining the routes and
wavelengths/spectrum for the connections to be established in the
network thanks to its RWA/RMLSA module. In optical networks a
similar role may be performed by the Path Computation Element (PCE)
[RFC4655]. Assuming a fixed grid network, the CDS receives requests
for lightpath establishment, and then computes a route and a
wavelength for that connection according to the current availabitity
of resources in the network, which is stored in the Traffic
Engineering Database (TED). The result of such computation (once
validated by the QoT estimator module) is used to establish the
connection by means of the RSVP-TE protocol [RFC3473]. Then, the CDS
can either take care itself of performing the updates to the TED, or
rely on the use of the OSPF-TE protocol for that aim, which implies
that the TED will be updated after some delay. Therefore, in the
latter case, the CDS may decide to assign to an incoming request a
resource that has already been assigned to another lightpath, but
for which the confirmation from OSPF-TE has not reached yet the
central TED. Hence, relying on OSPF-TE to update the TED leads to
increasing the blocking probability when compared to a scenario
where the TED is directly updated by the CDS.
In [Rod2013] a cognitive mechanism based on an elapsed times matrix
(ETM) heuristic has been proposed, which aims at avoiding the
selection of resources which have been recently assigned by the CDS
(or a PCE) to another request, by exploiting recent past history (a
situation that may arise, for instance, during a restoration process
triggered to deal with a link failure). Please note that this
technique can be easily introduced in stateless PCEs without
requiring protocol extensions, as it only implies the modification
of the underlying PCE algorithm.
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4.3. Virtual Topology Design and Reconfiguration
A further example of the potential of cognition in optical networks
is related to the virtual topology design module of CDS. A multi-
objective genetic algorithm has been proposed in [Fer2012] to design
impairment-aware and survivable virtual topologies, with the aim of
reducing both the energy consumption and the network congestion. In
a single execution, the algorithm provides several solutions with
different trade-offs in terms of the two optimization objectives
just mentioned (i.e., a collection of virtual topologies which
constitute a good estimate of the so-called Pareto Optimal Set).
This method has been further enhanced with two cognitive techniques
based on the utilization of memory to remember solutions
successfully used in the past: a) a Tabu list to remember
connections with low QoT and b) a learning process to select the
most appropriate knowledge for the current network state from that
memory.
The introduction of cognitive techniques in virtual topology design
and reconfiguration leads to significant savings in terms of the
total cost of ownership compared to conventional methods. For
instance, the case study in [Fer2012] shows that capital and
operational expenditures can be respectively reduced by up to 20%
and 25%.
5. Implications on the Control Plane
A key building block of a Cognitive Capable Optical Network is the
Control Plane (CP) that complements the Cognitive Decision System
(CDS). Whatever the chosen architectural approach, current CP
solutions need to be enhanced to enable the full potential of the
cognitive processes running in the CDS. A brief description of the
tasks that the CP must perform in order to achieve such a result is
provided in the following.
5.1. Disseminate network configuration information
The CP should control the network configuration providing a
description of the network in terms of physical components,
topology, resources availability, and configuration of the used
resources. This description has to be continuously kept updated by
the CP, by notifying to the cognitive entities any change occurring
in the network configuration. In both centralized and distributed
cognitive architectures this task can be performed by the OSPF-TE
protocol [RFC4203] of the GMPLS suite. The OSPF-TE protocol has to
be extended to describe the status of the fixed and configurable
parameters of the devices inside a node or associated with a link
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(e.g., amplifiers, filters). Regarding the configuration of the
devices and the physical components, the CP has different ways to
collect this information before disseminating it; indeed, it can be
statically provided by the network operator, or it can be
dynamically discovered by means of the Link Management Protocol
(LMP) [RFC4204]. With respect to the disseminated information,
network scalability can potentially be an issue, since OSPF-TE may
have a lot of data to advertise; however, this can be mitigated by
choosing an appropriate number of parameters needed by the cognitive
system and encoding them accordingly. In addition, if a centralized
approach is considered, it could be noted that the central cognitive
entity, (this is the CDS), should be aware of resource availability,
since it is such element itself that makes the decisions on the
devices to configure. Nevertheless, OSPF-TE utilization remains
paramount to provide the initial configuration of the devices and to
update the database of the CDS when links are not available anymore.
Moreover, OSPF-TE is a widely used, standardized, and stable
protocol; extending it to support the cognitive features is a safer
solution than implementing these new features as new in a non-
standard solution.
5.2. Feed the cognitive processes with network data and statistics
Cognitive processes exploit traffic status information and optical
quality of transmission measurements, in order to perform effective
decisions during lightpath setup and to foresee potential service
disrupting situations. There are different techniques to retrieve
the aforementioned information and different networking protocols
are available to manage this task (i.e., SNMP [RFC1157], RMON
[RFC4502]). The approach proposed in [Sir2012] leverages on a
monitoring agent located on each node that collects information
about monitored parameters (e.g., power, BER, OSNR, traffic) by
querying the physical nodes. This information is sent to a
monitoring server located in the cognitive node that collects the
information and stores them in a database, which is accessible by
the cognitive processes. Moreover, the cognitive entity can also
receive alarms from monitoring agents when a critical (or
potentially critical) situation at the physical layer faces up.
5.3. Implement the decisions of the cognitive processes on the device
The CP has to reserve the resources on the basis of the decisions
made by the cognitive processes running in the CDS. Also in this
case, in both centralized and distributed cognitive architectures,
this task can be performed by a GMPLS protocol, namely the RSVP-TE
protocol [RFC3473]. On this account, the RSVP-TE protocol must be
extended to carry the instructions that the cognitive entities have
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produced for each device throughout the path. In particular, the
PATH message requires an extension to encode the configuration
parameters of each device on the path (e.g., the modulation format
for the transmitters, the port and connectivity parameters of the
OXC switching matrices). At the end of this process, via non-
standard communications, the CP may also be able to notify the CDS
if the required operation has been successfully performed and, in
case of failure, report the issue that caused such a failure.
In the proposed framework, the CDS should be able to trigger the re-
optimization of the resources, in order to achieve a better
efficiency in terms of utilization, energy efficiency, etc. Complete
information of network status is needed to perform this task. On
this account, a distributed approach cannot be easily adopted for
such a re-optimization, since the information disseminated by OSPF-
TE does not allow the construction of a stateful database. For what
concerns the centralized approaches, a standard Path Computation
Element (PCE)-based solution [RFC4655] would not be suitable to
carry out this task being the PCE's role to answer to path requests
forwarded by source nodes. Although the original PCE architecture
was not thought to be able to autonomously trigger lightpath
activation, some recent standardization efforts are trying to
address this issue by means of extensions to PCEP [Ali2013]; such
mechanisms should allow a stateful PCE to remotely initiate
lightpath setup. However, by the time being, the discussion within
IETF is still at an early stage. A feasible centralized
implementation based on GMPLS is the one proposed in [Sir2012], in
which the CDS itself can initiate a lightpath setup and trigger the
RSVP-TE reservation. Once the reservation has been completed, the CP
sends a response to the CDS notifying if the required operation has
been successfully performed and, in case of failure, reporting the
issue that caused such a failure.
The process of evolution of the CP may be directed to a joint
control of the optical and the packet domains. In this perspective,
an SDN-based controller may cooperate with the cognitive entities
and the CP of an optical network [Das2012]. The cognitive entities
could relieve the SDN controller from the high overhead due to the
complexities at the photonic layer. In particular, they could
provide to the controller already signaled and optically feasible
lightpaths, whose computations are optimized on a multi-layer
fashion and tailored on the basis of the needs of the packet layer.
6. Contributing Authors
This document was the collective work of several authors. The text
and content of this document was contributed by the editors and the
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co-authors listed below (the contact information for the editors
appears in appropriate section and is not repeated below):
Yabin Ye
Huawei Technologies Duesseldorf GmbH
Riesstr. 25, C-3.0G
80992 Munich - Germany
Phone: +49-89-1588344052
Email: yeyabin@huawei.com
Dimitrios Klonidis
Athens Information Technology Center (AIT)
0.8km Markopoulou Av.
19002 Peania, Athens - Greece
Phone: +30-210-6682773
Email: dikl@ait.gr
Andrzej Tymecki
Orange Labs Poland
ul.Czere.niowa 8
21-040 Swidnik, Poland
Phone: +48-81-5244467
Email: Andrzej.Tymecki@orange.com
Idelfonso Tafur Monroy
Technical University of Denmark (DTU)
Oerstedsplads 343
DK-2800 Kgs. Lyngby, Denmark
Phone: +45 45255186
Email: idtm@fotonik.dtu.dk
7. Security Considerations
TBD
8. IANA Considerations
This memo includes no request to IANA.
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9. References
9.1. Informative References
[RFC1157] J. Case, M. Fedor, M. Schoffstall, and J. Devin, "A Simple
Network Management Protocol," IETF RFC 1157, May 1990.
[RFC3473] L. Berger, "Generalized Multi-Protocol Label Switching
(GMPLS) Signaling Resource ReserVation Protocol-Traffic
Engineering (RSVP-TE) Extensions," RFC 3473, January 2003.
[RFC4203] K. Kompella and Y. Rekhter, "OSPF extensions in support of
Generalized Multi-Protocol Label Switching (GMPLS)", IETF
RFC 4203, October 2005.
[RFC4204] J. Lang, "Link Management Protocol (LMP)", IETF RFC 4204,
October 2005.
[RFC4502] S. Waldbusser, "Remote Network Monitoring Management
Information Base Version 2," IETF RFC 4502, May 2006.
[RFC4655] A. Farrel, J.-P. Vasseur, and J. Ash, "A Path Computation
Element (PCE)-Based Architecture", RFC 4655, August 2006.
[Tav2011] W. Tavernier, D. Papadimitriou, D. Colle, "Learning
Capable Communication Network (LCCN) Problem Statement",
IETF draft, January 2011, draft-tavernier-irtf-lccn-
problem-statement-01.txt.
[Beh2013] M. Behringer, M. Pritikin, S. Bjarnason, and A. Clemm, "A
Framework for Autonomic Networking", IETF draft, October
2013, draft-behringer-autonomic-network-framework-01.txt.
[Tho2006] R.W. Thomas, D.H. Friend, L.A. DaSilva, and A.B.
MacKenzie, "Cognitive networks: Adaptation and learning to
achieve end-to-end performance objectives," IEEE
Communications Magazine, pp. 51-57, Dec. 2006.
[Mov2012] Z. Movahevic, M. Ayari, R. Langar, and G. Pujolle, "A
survey of autonomic network architectures and evaluation
criteria," IEEE Communications Surveys & Tutorials, vol.
14, no. 2, pp. 464-490, Second Quarter 2012.
[Ger2012] O. Gerstel, M. Jinno, A. Lord, and S.J.B. Yoo, "Elastic
optical networking: a new dawn for the optical layer?,"
IEEE Communications Magazine, vol. 50, no. 2, pp. s12-s20,
February 2012.
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[deM2013] I. de Miguel, R. J. Duran, T. Jimenez, N. Fernandez, J. C.
Aguado, R. M. Lorenzo, A. Caballero, I. Tafur Monroy, Y.
Ye, A. Tymecki, I. Tomkos, M. Angelou, D. Klonidis, A.
Francescon, D. Siracusa, E. Salvadori, "Cognitive Dynamic
Optical networks", Journal of Optical Communications and
Networking, vol. 5, no. 10, pp. A107-A118, Oct. 2013.
[Cha2010] C.C.K. Chan, Optical Performance Monitoring - Advanced
Techniques for Next-Generation Photonic Networks,
Elsevier, 2010.
[Kli2010] D. Kliazovich, F. Granelli, and N.L.S. Da Fonseca,
"Architectures and cross-layer design for cognitive
networks" in Handbook of sensor networks. World Scientific
Publishing Co, 2010, Chap. 1.
[Zer2010] G.S. Zervas and D. Simeonidou, "Cognitive optical
networks: Need, requirements and architecture," in Proc.
ICTON 2010, paper We.C1.3.
[Wei2012] W. Wei, C. Wang, and J. Yu, "Cognitive optical networks:
key drivers, enabling techniques, and adaptive bandwidth
services," IEEE Communications Magazine, pp. 106-113, Jan.
2012.
[Das2012] S. Das, G. Parulkar, and N. McKeown, "Why OpenFlow/SDN can
succeed where GMPLS failed", Technical Digest ECOC 2012,
paper Tu.1.D.1.
[Wit2011] I.H. Witten, E. Frank, and M.A. Hall, Data Mining:
Practical Machine Learning Tools and Techniques (Third
Edition). Morgan Kaufmann Publishers, 2011.
[Jim2013] T. Jimenez, J.C. Aguado, I. de Miguel, R.J. Duran, M.
Angelou, N. Merayo, P. Fernandez, R.M. Lorenzo, I. Tomkos,
and E.J. Abril, "A cognitive quality of transmission
estimator for core optical networks," Journal of Lightwave
Technology, vol. 31, no. 6, pp. 942-951, March 2013.
[Rod2013] I. Rodriguez, R.J. Duran, D. Siracusa, I. de Miguel, A.
Francescon, J.C. Aguado, E. Salvadori, and R.M. Lorenzo,
"Minimization of the impact of the TED inaccuracy problem
in PCE-based networks by means of cognition," in Proc.
ECOC 2013, paper We.4.E.2.
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[Fer2012] N. Fernandez, R.J. Duran, I. de Miguel, N. Merayo, J.C.
Aguado, P. Fernandez, T. Jimenez, I. Rodriguez, D.
Sanchez, R.M. Lorenzo, E.J. Abril, M. Angelou, and I.
Tomkos, "Survivable and impairment-aware virtual
topologies for reconfigurable optical networks: a
cognitive approach," in Proc. RNDM 2012, pp. 183-189.
[Fer2013] N. Fernandez, R.J. Duran, E. Palkopoulou, I. de Miguel, I.
Stiakogiannakis, N. Merayo, I. Tomkos, and R.M. Lorenzo,
"Techno-economic advantages of cognitive virtual topology
design," in Proc. ECOC 2013, paper Tu.3.E.6.
[Sir2012] D. Siracusa, E. Salvadori, A. Francescon, A. Zanardi, M.
Angelou, D. Klonidis, I. Tomkos, D. Sanchez, R.J. Duran,
and I. de Miguel, "A control plane framework for future
cognitive heterogeneous optical networks," in Proc. ICTON
2012.
[Ali2013] Z. Ali, S. Sivabalan, C. Filsfils, R. Varga, and V. Lopez,
"Path Computation Element Communication Protocol (PCEP)
Extensions for remote-initiated GMPLS LSP Setup", IETF
draft (draft-ali-pce-remote-initiated-gmpls-lsp-01.txt),
July 2013
10. Acknowledgments
This work is supported by the European Commission (EC) Seventh
Framework Programme (FP7) CHRON project (Grant No. 258644).
This document was prepared using 2-Word-v2.0.template.dot.
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Authors' Addresses
Domenico Siracusa
CREATE-NET
v. alla Cascata 56D
38123 Trento - Italy
Phone: +39-0461-408400
Email: domenico.siracusa@create-net.org
Antonio Francescon
CREATE-NET
v. alla Cascata 56D
38123 Trento - Italy
Phone: +39-0461-408400
Email: antonio.francescon@create-net.org
Elio Salvadori
CREATE-NET
v. alla Cascata 56D
38123 Trento - Italy
Phone: +39-0461-408400
Email: elio.salvadori@create-net.org
Ramon J. Duran
Universidad de Valladolid
ETS Ingenieros de Telecomunicacion - Campus Miguel Delibes
Paseo de Belen 15, 47011 Valladolid - Spain
Email: rduran@tel.uva.es
Ignacio de Miguel
Universidad de Valladolid
ETS Ingenieros de Telecomunicacion - Campus Miguel Delibes
Paseo de Belen 15, 47011 Valladolid - Spain
Email: ignacio.miguel@tel.uva.es
Ruben M. Lorenzo
Universidad de Valladolid
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ETS Ingenieros de Telecomunicacion - Campus Miguel Delibes
Paseo de Belen 15, 47011 Valladolid - Spain
Email: rublor@tel.uva.es
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