# Experimental Demonstration of Partially Disaggregated Optical Network
Control
Using the Physical Layer Digital Twin

Giacomo Borraccini, , Stefano Straullu, Alessio Giorgetti,
Renato Ambrosone, Emanuele Virgillito, ,
Andrea D’Amico, , Rocco D’Ingillo, , Francesco Aquilino, Antonino Nespola,
Nicola Sambo, Filippo Cugini, and Vittorio Curri Giacomo Borraccini, Renato
Ambrosone, Emanuele Virgillito, Andrea D’Amico, Rocco D’Ingillio and Vittorio
Curri are with the Department of Electronics and Telecommunications (DET),
Politecnico di Torino, Turin 10129, Italy (email:
<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>vittorio.curri@polito.it).Renato Ambrosone is also
with GARR Consortium, Rome 00185, Italy.Stefano Straullu, Francesco Aquilino
and Antonino Nespola are with LINKS Foundation, Turin 10138, Italy (email:
<EMAIL_ADDRESS><EMAIL_ADDRESS>antonino.nespola@linksfoundation.com).Alessio Giorgetti is with IEIIT-CNR,
Pisa 56122, Italy (email: alessio.giorgetti@ieiit.cnr.it).Nicola Sambo is with
the Scuola Superiore Sant’Anna, Pisa 56127, Italy (email:
n.sambo@sssup.it).Filippo Cugini is with the CNIT, Pisa 56124, Italy (email:
filippo.cugini@cnit.it).

###### Abstract

Optical communications and networking are fast becoming the solution to
support ever-increasing data traffic across all segments of the network,
expanding from core/metro networks to 5G/6G front-hauling. Therefore, optical
networks need to evolve towards an efficient exploitation of the
infrastructure by overcoming the closed and aggregated paradigm, to enable
apparatus sharing together with the slicing and separation of the optical data
plane from the optical control. In addition to the advantages in terms of
efficiency and cost reduction, this evolution will increase the network
reliability, also allowing for a fine trade-off between robustness and maximum
capacity exploitation. In this work, an optical network architecture is
presented based on the physical layer digital twin of the optical transport
used within a multi-layer hierarchical control operated by an intent-based
network operating system. An experimental proof of concept is performed on a
three node network including up to 1000 km optical transmission, open re-
configurable optical add & drop multiplexers (ROADMs) and white-box
transponders hosting pluggable multi-rate transceivers. The proposed solution
is based on GNPy as optical physical layer digital twin and ONOS as intent-
based network operating system. The reliability of the optical control
decoupled by the data plane functioning is experimentally demonstrated
exploiting GNPy as open lightpath computation engine and software optical
amplifier models derived from the component characterization. Besides the
lightpath deployment exploiting the modulation format evaluation given a
generic traffic request, the architecture reliability is tested mimicking the
use case of an automatic failure recovery from a fiber cut.

###### Index Terms:

Open optical networks, software-defined networking, multi-vendor,
disaggregation.

##  I Introduction

Service providers and network operators are showing interest in disaggregated
optical networks, vendor-neutral control and management and multi-vendor
interoperability as a way to overcome vendor lock-in and save capital outlay
[1, 2]. One specific example is the separation of control, data, and
management planes, which has become increasingly important in recent years.
Disaggregation may help operators to overcome vendor-lock-in at the control
plane level when used in conjunction with the specification of control and
management of transponders, transceivers (TRXs), and re-configurable optical
add & drop multiplexers (ROADMs). A considerable amount of effort is going
into creating standardized data models that vendors and operators can all
utilize. The Yet Another Next Generation (YANG) [3, 4] data modeling has
emerged as the preferred language for interacting with the control and
management system. The Network Configuration (NETCONF) protocol, which was
established by the Internet Engineering Task Force, supports YANG (IETF) [5].
Open and disaggregated networking is being defined by a number of consortia
and initiatives, including OpenConfig [6], OpenROADM [7], and Telecom Infra
Project (TIP) [8], which include major operators, service providers, and
manufacturers. For the definition of transponders, the OpenConfig consortium
has given the YANG data format (e.g., [9]) and various solutions on the market
currently support the OpenConfig YANG data model. In addition, there are
several works in the literature that show how to configure network elements
(NEs) or subsystems using NETCONF/YANG, transport performance parameters and
reconfigure these elements according to them. With the aid of specially
created data models, it has been demonstrated that NETCONF and YANG can
configure and monitor transponders [4, 10, 11]. The authors of [12] created
two different types of agents: an OpenConfig-based transponder agent and an
OpenROADM-based line system agent. YANG is used in [13] to enable device
configuration, monitoring of operational data and physical parameters, local
control loops for basic parameter reconfiguration/tuning and notifications
incorporating alarms. In [14], the authors demonstrate disaggregation allowing
automated optical path protection through a software-defined networking (SDN)
controller. In [15], a sliceable transponder supporting spectrum and space
dimensions is supplied with an implementation of an OpenConfig-based agent
(i.e., local controller of a device). Telemetry applications based on the gRPC
protocol [16] have also been modeled using YANG. Exploiting NETCONF and other
industry-standard protocols, the open ONOS controller in [17] performs
discovery and control of the network topology. The quality of transmission
(QoT) estimation is another feature that has to be addressed, particularly for
lightpath (LP) provisioning and maintenance. An LP is a transmission channel
in the optical domain, defined by the physical path between a source and a
destination node, which does not include any optical-electronic-optical
conversion, and the frequency slot used, thus implying the wavelength
continuity. As far as coherent transmission technology is concerned, the
ability to recover the constellation phase noise by means of carrier phase
estimation (CPE) algorithms and the linear mapping between optical field and
electric received signals allows to model the optical transmission as a an
additive white and Gaussian noise (AWGN) channels, enabling optical
transmission through transparent LPs using coherent technology. In this
condition, the generalized signal to noise ratio (GSNR) [18] can be employed
as a unique figure of merit for QoT. The GSNR is determined by dividing the
power of the channel being tested by the total of the accumulated amplified
spontaneous emission (ASE) noise caused by optical amplifiers and the non-
linear interference (NLI) impairment caused by fiber propagation. The AWGN
abstraction of LPs has been implemented in the TIP’s GNPy, an open-source and
vendor-neutral QoT estimator [8, 19, 20, 21]. In order to enforce fundamental
transmission parameters on transponders and ROADMs for LP provisioning or
restoration, SDN controllers have so far used QoT estimators, even if further
improvements are required to adequately handle the optical data plane
technology’s complexity. For example, there are no erbium doped fiber
amplifier (EDFA) drivers available in the ONOS repository, and existing ONOS
implementations do not take into account these NEs, which impact significantly
on the overall QoT. Considering the market trend towards the deployment of
optical multi-band transmission, expanding SDN controllers to include such
optical amplifiers is a required feature to cope with scalability and
performance challenges. Moreover, the inclusion of open packet-optical nodes
housing pluggable TRXs has an additional impact on complexity and resilience
since it needs the SDN controller to operate on the setup of new parameters,
such as at the packet level.

The authors experimentally verified the functioning of a partially
disaggregated optical network with a linear topology (up to 1400 km of
distance) [22]. Furthermore, the functioning of the physical layer digital
twin and the optical network controller within the overall infrastructure
orchestration has also been highlighted in [23] and the presented architecture
has been implemented on an experimental multi-vendor triangular-topology
system.

In this work, a partially disaggregated optical network architecture is
systematically described and fully validated on the same multi-vendor
triangular-topology network, controlling both open packet-optical nodes, i.e.
packet switches hosting pluggable coherent TRXs, and a completely effective
optical data plane encompassing ROADM whiteboxes and optical amplifiers. The
ONOS SDN controller exploits NETCONF protocol [24, 25] with OpenConfig vendor-
neutral YANG data models for the control of pluggables and ROADMs from
multiple vendors. In addition, ONOS relies on the GNPy for GSNR estimation
[26]. The GNPy tool is also leveraged for the configuration of the optical
amplifiers, guaranteeing full visibility and control of the analog parameters
(e.g., gain profile) while freeing the SDN controller from the burden of
controlling all network EDFAs. In this way, the SDN controller can maintain
visibility of network parameters, while the GNPy tool can practically evolve
as a physical layer digital twin solution for the entire optical
infrastructure.

The article consists of four main sections. In Sect. II, an architecture for
the control of an open and disaggregated optical network is presented in terms
of involved actors and their interactions, providing further details regarding
the functioning of the optical control plane exploiting a cognitive approach,
and of the data plane, in the respective subsections. In Sect. III, the
experimental apparatus set up to validate the proposed network architecture is
reported, focusing on the instrumentation, the various implementation choices
and the adopted precautions. In Sect. IV, the experimental results obtained
during the various steps of the validation are reported and commented on. In
particular, it is observed: the cognitive process of the control plane as
regards the characterization of the physical layer and the optimization of the
working point of the amplifiers, the transmission performance of the two paths
present in the topology, the functioning of the deployment and recovery
procedure of LPs with relative time lapse measurements. Finally, in Sect. V
the conclusions are outlined.

Figure 1: Abstract scheme of the open and disaggregated optical network
architecture, designed to independently manage the optical data and control
plane.

##  II Network Architecture

The proposed optical network architecture is designed to work in an open and
disaggregated context, aiming to achieve interoperability among multi-vendor
equipment and decouple the optical data and control planes. Specifically, the
target approach is a partially disaggregated management [27] in which each
node-to-node optical link is typically provided by a single vendor. Thus, the
NEs of a line are meant to be handled by a single controller, collapsing the
management to a single element from a network point of view. The
representation in Fig. 1 schematizes an abstract network structure.

Starting from the physical layer (PHY), the optical hardware infrastructure is
composed by TRXs, ROADM whiteboxes, optical fiber spools and optical
amplifiers. A couple of booster (BST) and pre-amplifier (PRE) is assumed to be
integrated in a single ROADM for each switching direction. In this network
architecture, a single optical line system (OLS) is defined as a ROADM-to-
ROADM optical line, thus including the BST and the PRE of the ROADMs at both
line terminals. Each amplification site along an OLS can host amplifiers of
different technologies, e.g. erbium-doped fiber amplifiers (EDFAs) or Raman
amplifiers, and telemetry devices, such as an optical time domain
reflectometer (OTDR), photo-diodes and optical channel monitors (OCMs). An
optical node consists of the set of TRXs and ROADMs placed in a specific
location within the geographical footprint of the optical network.

The optical equipment is managed by the cooperation of four different software
modules: (i) the network operative system (NOS); (ii) an optical line
controller (OLC) for each OLS composing the optical network; (iii) the
physical layer aware simulation environment (PLASE); and (iv) the open optical
network controller (OONC). The communication among the modules, TRXs and
ROADMs is performed exploiting open standard interfaces and protocols (REST,
NETCONF, etc..).

The NOS is aware of the status of the optical nodes and their connections,
building an abstraction of the network topology and directly controls each TRX
and ROADM within the network. Furthermore, the NOS must properly manage
different types of failure detected at the PHY, both from nodes and lines.
Each OLC is responsible for the management of the corresponding OLS, which is
generally provided by a specific vendor. The OLC communicates through defined
application programming interfaces (APIs) with all the amplification sites of
the OLS and the BST and the PRE, collecting telemetry information by means of
device polling, configuring the amplifiers’ working point and notifying status
information derived from interrupt management. The PLASE represents the
building block of the network architecture within which all the intelligence
regarding the PHY is collected. In particular, the PLASE stores data related
to the PHY, such as datasets or amplifier/ROADM/TRX/fiber characterizations.
The digital twin of the physical layer (PHY-DT) represents the central element
of the PLASE, including the model of the physical layer and allowing to
simulate the behavior of the system. On top of this, several computational
algorithms are run to fulfill different tasks which rely on the PHY
information knowledge. The PLASE directly communicates both with the OLCs and
the OONC. With respect to the OLCs, the PLASE collects telemetry data from the
NEs, performing fundamental operations such as the working point optimization
of a specific OLS and the LP computation engine (L-PCE) [28].

The OONC implements the system north-bound interface exposed towards the
network users. This module orchestrates the deployment process, for instance,
it transparently realize the LP allocation and recovery in the optical
network, interacting with the other software modules (i.e., the NOS and the
PLASE). Specifically, the OONC constructs the description of the spectrum grid
by supplying a single configuration for the active channel frequencies via a
static external configuration recording the center frequencies for all optical
DWDM signals. As a result of the topology abstraction offered by the NOS, the
routing space is then created and contains details on all conceivable pathways
inside the network as well as the availability of the channel wavelength along
each path. By making use of such structures, the OONC carries out the routing
spectrum assignment (RSA), helped by the PLASE transmission performance
indicators, in response to incoming traffic requests from outside pairs of
source-destination nodes. In the following sections, the behaviour of the
proposed network architecture is illustrated, focusing individually on the
optical control and data plane operations and showing how, in our perspective,
such two aspects may be decoupled and independently managed from a procedural
point of view.

Figure 2: Qualitative time line of the proposed cognitive optical control
plane operation that leads an OLS to be ready for use.

###  II-A Cognitive Optical Control Plane

The optical control plane is in charge to manage the optical equipment in
order to maximize the exploitation of the installed resources in terms of
transmission performance. This task requires the possibility to choose the
most advantageous strategy for the amplifiers’ working point setting, while
minimizing the allocated margins [29]. The latter condition is directly
related to the degree of knowledge of the PHY’s devices, both lumped (optical
switches, connectors, EDFAs) and components inducing distributed effects such
as fibers or Raman amplifiers. In particular, the effect of lumped losses and
fibers can be appreciated only in field by means of the indirect estimation of
their properties. In this perspective, the network provisioning can be
performed downstream of a _probing_ procedure using the available monitors and
telemetry in order to completely characterize the PHY, in order to reduce the
margin allocation. The _cognitive_ property is the main feature of a class of
optical networks bearing the same name [30, 31]. In the network context
described above, the optical control plane is represented by the collection of
all the OLCs supervised by the PLASE. In the following, the procedural steps
that bring to the network provisioning are described focusing on the case of a
single OLS (Fig. 2). The procedure can be repeated over all the OLS, with no
loss of generality, given the partial disaggregation context. However, the
process that leads the OLS to be ready to perform network operations is
entirely dependent on the choices dictated by the OLS vendor, especially in
the type of data that the OLC exposes.

After the installation and before starting the transmission operations, an OLS
undergoes a tuning procedure targeting the definition of the working point of
each amplifier along the line. This procedure is meant as an automated probing
of the OLS, aiming to achieve a more accurate knowledge of the PHY, capturing
the behaviour of the system directly from the field and consequently adjusting
the model adopted by the PHY-DT within the PLASE. Such procedure is composed
of three steps:

  * •

Configuration of the equipment using predefined settings;

  * •

Telemetry measurements of the quantities of interest;

  * •

Device polling in order to collect the information.

A dedicated representational state transfer (REST) API is used to transfer
data from the OLC to the PLASE and vice versa. The PLASE estimates the lumped
losses and the properties of the fibers matching the collected measurements
with the configuration of the equipment during the probing procedure,
completing the characterization of the PHY. At the end of the probing
procedure led by an OLC, the related PHY descriptions of amplifiers, TRXs and
ROADMs are stored in a static database shared with the PLASE.

Figure 3: Qualitative time line of the proposed optical data plane operation:
establishment of a connection following a LP request and a recovery after a
failure detection.

On top of such a detailed PHY model, the OONC provides the topology
abstraction, describing the available physical connections between TRXs and
ROADMs, thus allowing the PLASE to identify the available OLS within the
network. Hence, the PLASE is able to build the complete network PHY topology
retrieving for each OLS the corresponding virtualization combining the virtual
topology, the data measured by the telemetry during the probing procedure and
the PHY descriptions. The PLASE defines the settings of each amplifier within
the specific OLS on the bases of the physical layer topology in order to
optimally match the transmission strategy. The devised architecture assumes
full spectral load operation at the basis of the optical control. This
corresponds to define the working point with respect to the worst case
scenario in terms of transmission performance. This assumption allows to
decouple the operation of the data and control planes at an operational level,
ensuring that the evaluation of the maximum modulation format cardinality for
a specific path and wavelength is conservative even after the deployment of
further LPs. Once the configuration is received, the OLS is ready to operate
as soon as the OLC sets the amplifiers at the design working point. During the
operations, the OLC periodically monitors the OLS status through the polling
of the telemetry devices.

###  II-B Optical Data Plane Operation

The optical data plane is in charge of managing the allocation of optical
connections, or LPs, between couples of source-destination (SRC-DST) nodes
based on the traffic requests coming from the application/IP layer. The choice
on the LP’s allocation is performed defining the characteristics of the
optical tributary signals in terms of occupied wavelength and modulation
format, and then arranging the corresponding assigned path along the optical
network [21]. Furthermore, a robust management of the previously allocated LPs
has to be guaranteed, both fulfilling new requests and intercepting or
possibly predicting failures, in order to promptly take countermeasures
against a possible LP out-of-service. In the described context, the operations
of the optical data plane are entrusted to the OONC supported by the PHY-DT
which performs the QoT estimation. In this work, the focus is, first, on the
deployment of a set of LPs between a couple of nodes to satisfy a given
generic traffic request. Secondly, the management of the traffic recovery is
addressed, providing for the automatic re-establishment of the lost
connections in case of hard failures.

The proposed qualitative time line of the operations is depicted in Fig. 3.
During the network provisioning, the NOS establishes a communication with all
the network optical nodes in order to identify which are the available
physical connections between the nodes and retrieve the status of the current
spectral occupancy in terms of already deployed LPs. The OONC receives this
information from the NOS and consequently draws a network topology abstraction
and the initialization of the routing space on the basis of the acquired
spectrum grid. The collection of such physical TRX/ROADM connections is sent
to the PLASE which are integrated with its OLS knowledge to create the
complete picture of the PHY topology. Once all the OLSs are ready to use, the
deployment of a connection is engaged by a generic traffic request to the
OONC, specifying the SRC/DST node couple and the required bit-rate. Then, the
nodes’ pair is forwarded to the PLASE which calculates the available maximum
modulation format through the PHY-DT for all the channels (wavelengths) and
for all the physical paths having the request nodes as end points, by
executing the L-PCE on the basis of the PHY topology and the TRX
characterizations. The OONC receives the evaluated modulation formats of all
the available paths and performs the RSA defining the characteristics and the
needed number of the LPs to deploy in order to satisfy the traffic request. In
particular, as preliminary step, the current routing space is used to check
the path feasibility for all the retrieved modulation formats, selecting only
the available channels that ensure wavelength continuity on a certain path.
The complete definition of each LP during the application of the RSA can
require constraints and specifications which depend on the adopted algorithm.
Finally, the LP is practically put into operation by properly configuring the
TRXs operational mode and the ROADMs directly through the NOS with the proper
interfaces. At the end of the operation, the NOS reports the outcome
(success/failure) to the OONC in order to correctly manage the traffic
request.

Moving ahead into the time line shown in Fig. 3, the process to counteract
hard-failures is described in the following. During the life-cycle of an
optical network, indeed, several types of failures may occur (e.g., due to
fiber cuts), causing LP out-of-service. Focusing on hard failures due to fiber
cut events, the detection of such events can be performed both at the OLS
level – with an interrupt received by the NOS through the OLC – or at the
node’s level – observing the TRX or ROADM telemetry stream directly at the NOS
level. In the proposed architecture, as first step, the NOS intercepts the
failure coming from the PHY at the optical node side and the OONC proceeds
modifying the network topology abstraction.

Figure 4: Experimental setup of the optical network set in laboratory,
including the spectra of the C-band WDM comb propagated through the various
optical nodes. ONOS holds the role of NOS and GNPy represents the PHY-DT.

This means that the failing link is removed from the available physical
connection list of the topology abstraction defined during the provisioning
phase. Then, this update is posted to the PLASE in order to keep track of the
PHY topology changes. At this point, the OONC tries to re-establish the
traffic lost due to the hard-failed link. This is done by repeating the
previously described deployment procedure for all the lost LPs, using the same
SRC/DST node pairs and original requested rates.

It is remarkable that this approach is carried out on a _best effort_ basis:
it may allocate the re-established LPs on different wavelengths accordingly to
the availability on the new physical paths. In addition, it may even split up
the original requested rates in multiple optical channels delivering lower
rates accordingly to the maximum modulation format available in the new
physical paths.

##  III Experimental Setup

An experimental setup has been built in LINKS Foundation’s photonics
laboratory, aiming to demonstrate the feasibility of such modular, open and
disaggregated optical network architecture by means of a proof of concept. The
experimental setup emulating an optical network is depicted in Fig. 4 and it
is composed by three nodes, each equipped with commercial TRXs and ROADMs and
connected by three different multi-span amplified OLSs, obtaining different
optical paths for the channels under test (CUTs). The TRXs are CFP2-ACO/DCO
coherent pluggables from Lumentum, programmed to generate 4 independent
signals (DP-QPSK or DP-16QAM modulated) and to continuously monitor the
related bit error rate (BER), providing an updated average value every 15
seconds. They are plugged in Cassini AS7716-24SC [32] boxes, an open network
packet-optical box built by Edgecore that can host line card slots to
incorporate ACO/DCO optical ports based on coherent digital signal processing
and optical TRXs from leading optical technology partners. The Cassini
whitebox is operated by the OcNOS operating system by IP Infusion, providing
configuration and monitoring facilities by means of NETCONF interfaces.

Figure 5: SNR estimation procedure using TRX B2B curves given the measured
BER.

A C-band wavelength division multiplexing (WDM) comb centered at 193.5 THz and
composed by 75 channels, 50 GHz spaced, modulated at 32 GBd each, is generated
at the Node A side: 4 CUTs, centered at 192, 193, 194 and 195 THz,
respectively, are generated by the TRXs, while a commercial wave shaper filter
(1000S from Finisar) is programmed to shape the output of an ASE noise source,
generating 71 channels that, coupled with the 4 CUTs, assemble the 75 channels
OLS spectral load with no loss of generality because of the large time
constant characterizing the physical effects within EDFAs.

ROADM 1 can be configured to add the 75 channels and to route them towards
Node C, either through Line 1 or Line 2: the former straight, LP 1, connects
Node A to Node C through 6 spans, each based on commercial EDFA operating in
constant gain mode and followed by a standard single mode fiber (SSMF) of 65
km nominal length. In the middle of the latter, ROADM 2 can drop the CUTs, so
that their BER can be evaluated, or forward them towards Node C. Line 2A and
Line 2B, composing LP 2, consist of 5 amplified SSMF spans of about 100 km
each. ROADM 3 finally drops the 4 CUTs either if they are propagated through
Line 1 or Line 2.

TABLE I: Physical Layer Characterization Results:
Complete Set of Retrieved Parameters LINE | SPAN |  | $\boldsymbol{L_{S}}$
---
[km]
| $\boldsymbol{C_{R}}$
---
[1/W/km]
| $\boldsymbol{D}$
---
[ps/nm/km]
| $\boldsymbol{l(0)}$
---
[dB]
| $\boldsymbol{l(L_{S})}$
---
[dB]
1 | 1 | 65.5 | 0.34 | 16.6 | 5.5 | 0.1
2 | 65.3 | 0.34 | 16.8 | 1.4 | 0.3
3 | 65.5 | 0.44 | 16.7 | 1.6 | 0.1
4 | 65.6 | 0.34 | 16.7 | 0.2 | 1.4
5 | 65.2 | 0.42 | 16.7 | 0.5 | 0.4
6 | 65.8 | 0.34 | 16.5 | 0.1 | 1.3
2A | 1 | 106.2 | 0.34 | 17.5 | 3.6 | 0.2
2 | 107.5 | 0.44 | 17.9 | 1.2 | 0.7
3 | 106.2 | 0.44 | 17.7 | 1.5 | 0.1
4 | 108.8 | 0.42 | 17.7 | 0.6 | 0.1
5 | 108.3 | 0.42 | 17.8 | 0.2 | 0.1
2B | 1 | 106.2 | 0.42 | 17.9 | 1.1 | 0.2
2 | 106.8 | 0.34 | 17.7 | 0.1 | 0.1
3 | 106.4 | 0.34 | 17.7 | 0.2 | 0.7
4 | 107.3 | 0.42 | 17.8 | 0.2 | 0.1
5 | 108.3 | 0.42 | 17.8 | 0.5 | 2.3
Figure 6: Physical layer characterization results: retrieved loss coefficient
functions for each optical fiber span within the network set in laboratory.

The software implementation includes ONOS, version 2.7.0, as NOS with some
additional custom features developed for this proof of concept. For the first
time, ONOS is capable to provide the frequency slot occupation per link with a
granularity of 12.5 GHz using new custom REST endpoints. Moreover, specific
drivers have been developed enabling the control of the TRXs through the OcNOS
operating system, including the configuration of the desired modulation
format. For this proof of concept, a QoT-driven approach is adopted, aiming to
favour a vendor-agnostic network management. As a QoT estimator, GNPy
represents the PHY-DT of the control system intelligence, providing the
optical propagation model within the PLASE. ONOS and the PLASE are hosted in
two different servers in order to emulate the cloud environment. ONOS’s server
is equipped with Intel(R) Pentium(R) CPU G860 @3.00 GHz and 16 GB of RAM. The
PLASE runs in a server powered by Intel(R) Core(TM) i7-4980HQ CPU @2.80 GHz
and 16 GB of RAM. The OONC is implemented as a Python framework orchestrating
the other software modules, exposing multiple REST endpoints developed based
on the Flask library. Each OLC exploits a Secure Shell (SSH) protocol, that
allows to open a control flow enabling to set and poll the EDFA’s working
parameters (e.g. gain, tilt) and performance monitors (e.g. OCM output, total
optical input and output power).

TABLE II: EDFA Optimal Working Point LINE | AMPLIFIER |  | G
---
[dB]
| T
---
[dB]
|  POUT
---
[dBm]
1 | BST | – | – | 21.8
ILA 1 | 15.0 | -0.1 | –
ILA 2 | 15.0 | -1.4 | –
ILA 3 | 15.0 | 0.0 | –
ILA 4 | 15.0 | 0.6 | –
ILA 5 | 15.7 | -1.0 | –
PRE | – | – | 20.0
2A | BST | – | – | 21.8
ILA 1 | 23.3 | -5.0 | –
ILA 2 | 22.1 | -5.0 | –
ILA 3 | 21.6 | -1.9 | –
ILA 4 | 22.9 | -1.0 | –
PRE | – | – | 23.0
2B | BST | – | – | 19.2
ILA 1 | 22.0 | -5.0 | –
ILA 2 | 22.2 | -4.8 | –
ILA 3 | 23.3 | -1.9 | –
ILA 4 | 23.0 | -1.4 | –
PRE | – | – | 20.0
TABLE III: Network Transmission Performance Validation Results | LP 1 (A ->C) | LP 2 (A ->B ->C)
---|---|---
| CUT 1
---
DCO
(192 THz)
| CUT 2
---
ACO
(193 THz)
| CUT 3
---
DCO
(194 THz)
| CUT 4
---
ACO
(195 THz)
| CUT 1
---
DCO
(192 THz)
| CUT 2
---
ACO
(193 THz)
| CUT 3
---
DCO
(194 THz)
| CUT 4
---
ACO
(195 THz)
| GNPy Prediction
---
[dB]
24.0 | 23.7 | 23.7 | 23.6 | 18.4 | 17.8 | 18.1 | 17.6
DP-QPSK (100G) | BER | 1.6e-8 | 9.5e-8 | 1.2e-08 | 8.6e-08 | 4.2e-05 | 1.9e-04 | 3.5e-05 | 1.4e-04
| GSNR
---
[dB]
27.1 | 24.6 | 27.5 | 24.7 | 19.1 | 17.7 | 19.2 | 18.0
| Margin
---
[dB]
3.1 | 0.9 | 3.7 | 1.1 | 0.6 | 0.0 | 1.1 | 0.4
DP-16QAM (200G) | BER | 3.9e-03 | 9.9e-3 | 4.2e-03 | 1.1e-02 | – | – | – | –
| GSNR
---
[dB]
26.3 | 25.0 | 26.0 | 24.7 | – | – | – | –
| Margin
---
[dB]
2.3 | 1.3 | 2.2 | 1.0 | – | – | – | –

##  IV Results

In the following, all the experimental results related to the validation of
the network optical transmission and the LP-recovery use-case are reported and
commented, illustrating the relevant observations and details in terms of
practical implementation. Both the PHY characterization and OLS control
optimization methodologies adopted in this work are taken from [26].

The experimental setup presents two different models of erbium-doped fiber In-
Line Amplifier (ILA) according to the nominal length of the specific fiber
span (65 or 100 km) and a BST and a PRE integrated in the Lumentum ROADMs.
Preliminarily, both the ILA models have been characterized at full spectral
load (C-band) in constant gain mode varying the gain and tilt parameters with
different values of total input power. Each collected dataset is used to train
a couple of artificial neural networks, abstracting and modelling the
behaviour of the gain profile and the introduced ASE noise profile of the
specific amplifier model. The ROADM’s BST and PRE have been similarly
characterized at full spectral load in constant output power mode for
different output power values varying the total power of the input C-band
spectrum. The software abstraction of these components is obtained for both
the applied gain and introduced ASE noise linearly interpolating in
logarithmic units the measured quantities. Both the TRX types (ACO/DCO) have
been characterized in the back-to-back (B2B) to obtain the BER vs. SNR curves
and consequently retrieving the related SNR threshold assuming $10^{-2}$ as
pre-FEC (forward error correction) BER threshold for each available modulation
format. In order to measure the GSNR, as graphically explained in Fig. 5, the
used method is to translate the measured BER from the Cassini by means of the
B2B characterization obtaining the corresponding SNR [33].

###  IV-A Physical Layer Characterization

The measurement process bringing to the definition of the PHY topology starts
with an OTDR analysis, performed for each fiber span measuring the fiber span
length, $L_{S}$, and the positions of eventual lumped losses, $l(z)$, present
along the specific span. After that, the BST and each in-line EDFA are set in
ASE mode providing at the corresponding output a full C-band ASE spectrum with
arbitrary shape. The latter is measured by OCMs at both terminals of each
fiber span. The two ASE power levels are defined accordingly to the
characteristics of the installed apparatus, such as EDFAs’ maximum total
output power and fiber span total losses, and to the telemetry sensitivity.

The PLASE characterizes each fiber span through an optimization strategy that
aims to reproduce the experimental measurements using the PHY optical
propagation model. The PHY parameters to estimate for a single fiber span are
the Raman efficiency scale factor, $C_{R}$, the loss coefficient function,
$\alpha(f)$, the input, $l(0)$, and the output connector losses, $l(L_{S})$,
and the eventual lumped losses detected by the OTDR, $l(0<z<L_{S})$.

The results of the PHY characterization are shown in Tab. I and Fig. 6. Given
a specific OLS within the optical network, each fiber span is characterized in
terms of the PHY parameters described above. In addition, the dispersion
coefficient, $D$, has been measured for each span before the complete
installation of the equipment. As per the data-sheet, the equivalent
representations retrieved by the characterization process correspond to the
properties of the SSMF type.

###  IV-B OLS Control & Transmission Performance

On the basis of the PHY topology, the PLASE optimizes the amplifier working
point feeding the PHY model with the retrieved parameters. The result of the
optimization process is reported in Tab. II. The optimization algorithm aims
to homogeneously maximize the GSNR along the whole band considering the
propagation model of the fibers and amplifiers. All the amplifiers integrated
within the ROADMs (BSTs and PREs) are set to work in constant output power
mode. Instead, the in-line amplifiers are set in constant gain mode. The
adjustment of the amplifier’s working point takes place by modifying the
device setting parameters, such as total output power, gain or tilt.

The evaluation of the network transmission performance obtained following the
described cognitive approach is operated by setting the amplifier working
point and estimating the GSNR of each CUT for both the LPs on the base of the
measured BER. Each GSNR estimation is compared to the GSNR value predicted by
GNPy, determining the related resulting margin. The latter is calculated
without considering any other contribution, as if the system worked at zero
margin. The summary of the experimental measurement campaign is presented in
Tab. III. The measure of the BER using the DP-16QAM modulation format has been
possible only for the case of the short path, LP 1. Observing the results, it
is remarkable the fact that DCO TRXs provide larger margins than ACO ones,
presenting a higher intrinsic device robustness. Comparing the results
inherent to the short path, LP 1, for the different modulation formats, it is
noted that the estimated GSNRs for the ACOs are comparable, as can be expected
given that the degradation introduced by the network does not change. Instead,
there is a non negligible variation for the DCO TRXs changing the modulation
format. Since the QoT is very high for these TRXs, this can be explained by
the working point location within the B2B curve, which is affected by a
greater uncertainty during the characterization phase. The GNPy GSNR
prediction is conservative in all the cases, obtaining a satisfactory result
by working with zero margin. Given these considerations, the degree of
flatness of the measured QoT also corresponds to the prediction on the basis
of the optimization criterion.

###  IV-C Lightpath Deployment & Recovery

In order to test the network behaviour, a use-case experiment is conducted
following the three main steps described in Fig. 3. During the boot phase,
ONOS instantiates a control plane connection with all the physical devices
(i.e., 2 Cassini boxes and 3 Lumentum ROADMs). Fig. 7 is a screenshot of the
the ONOS GUI showing all connected devices and the considered optical links.
Two emulated devices are included (i.e., ASE/OSA), acting as logical
termination points of the 71 ASE-shaped channels. Given the topology and
spectral occupancy information provided by ONOS, thanks to the custom
developed ONOS endpoints, the OONC is able to summarize the network status in
terms of physical paths and free channels, initializing the routing space.

Figure 7: ONOS topology (optical nodes and links) of the experimental setup.

For the considered use-case, a traffic request of 400G between the nodes A and
C is generated and sent to the OONC. The PLASE replies with the modulation
formats computed for each channel frequency for all the paths having the same
SRC and DST nodes. The OONC carries out the RSA algorithm to select the
available channels with the highest available modulation format cardinality.
The chosen LPs are sent to ONOS through the developed REST interfaces, which
operatively deploy the LP on the devices. Based on the transmission
performance simulation shown in Tab. III, the traffic request is satisfied
with two DP-16QAM on the shorter line, Line 1. All the exchanged REST requests
regarding the deployment of the LPs satisfying the given traffic request are
described in Fig. 8.

As last step, a hard failure is emulated on Line 1 with the two active
connections. Once ONOS notifies to the OONC the change of the PHY, the
topology abstraction is updated and the lost traffic is estimated. Thus, the
LP deployment procedure is triggered on the updated topology, trying to
recover the lost traffic between the nodes A and C. As the DP-16QAM is not
feasible along the longer line, in order to recover the whole 400G lost
traffic, 4x DP-QPSK intents are chosen. All the messages exchanged during the
LP recovery process are shown in Fig. 9.

Figure 8: REST requests exchanged between the software modules to satisfy a generic traffic request coming from the application/IP layer (by Wireshark). Figure 9: REST requests exchanged between the software modules to recover the lost traffic after a hard-failure (by Wireshark). TABLE IV: Lightpath Recovery Time Lapse Measurements Interaction | Time [sec]
---|---
Topology Update | 0.017
Lost Traffic Estimation | 0.012
L-PCE | 6.580
Lightpath Establishment | 4.870
Total Recovery | 11.708

The time lapse measurements regarding the control interactions performing the
LP recovery are summarized in Tab. IV. These measurements refer to a single
200G LP recovery (2x DP-QPSK deployed LPs), which are comparable with the
initial case of 400G LP deployment (2x DP-16QAM deployed LPs). The two most
significant time-consuming processes are the L-PCE and LP establishment,
spending roughly 4.5 and 6.5 seconds, respectively. The first one is a CPU-
bound process where the dominant contribution is given by the computation of
the various artificial neural networks simulating the optical amplifier
behaviour. Because of this, a reduction of roughly one order of magnitude in
the execution time is expected using a dedicated hardware and a further
software optimization. The LP establishment process is consistent with other
experiments available in literature, where most of the time is required by the
specific device to set the connection [25]. Also this value can be decreased
by means of hardware updates and next generation transceivers.

##  V Conclusion

An architecture for the control of an open and disaggregated optical network
has been presented in terms of the players involved and their interactions,
highlighting the independent operation of the optical control and data planes.
As a proof of concept, a multi-vendor triangular-topology optical network
experimental setup has been built to verify the suggested network
architecture. The main adopted management strategies are the use of the
optical network at full spectral load and a QoT-driven cognitive vendor-
agnostic process of the control plane with regards to the characterization of
the physical layer and the optimization of the amplifiers’ working point. The
implementation of the software framework involves ONOS as network operative
system and GNPy as physical layer digital twin. The tested features of the
proof of concept were the transmission performance of the two LPs in the
topology as well as the operation of the deployment and recovery procedure
through relative time lapse measurements, obtaining deployment time intervals
compatible with recent analogous works.

## Acknowledgments

The authors wish to thank Lumentum, Telecom Infra Project, Cisco, IP Infusion,
Open Network Foundation for providing hardware and software.

## References

  * [1] “https://code.facebook.com/posts/1977308282496021/an-open-approach-for-switching-routing-and-transport/.”
  * [2] E. Riccardi, P. Gunning, O. G. de Dios, M. Quagliotti, V. López, and A. Lord, “An operator view on the introduction of white boxes into optical networks,” _Journal of Lightwave Technology_ , vol. 36, no. 15, pp. 3062–3072, 2018.
  * [3] M. Bjorklund, “YANG - a data modeling language for the network configuration protocol (NETCONF),” IETF RFC 6020.
  * [4] M. Dallaglio, N. Sambo, F. Cugini, and P. Castoldi, “Control and management of transponders with NETCONF and YANG,” _IEEE/OSA JOCN_ , vol. 9, no. 3, pp. B43–B52, March 2017.
  * [5] R. Enns, M. Bjorklund, J. Schoenwaelder, and A. Bierman, “Network configuration protocol (NETCONF),” IETF RFC 6241, June 2011.
  * [6] “http://www.openconfig.net.”
  * [7] “http://www.openroadm.org.”
  * [8] “https://telecominfraproject.com/.”
  * [9] “https://www.fujitsu.com/us/products/network/products/1finity-t600/.”
  * [10] N. Sambo, K. Christodoulopoulos, N. Argyris, P. Giardina, C. Delezoide, A. Sgambelluri, A. Kretsis, G. Kanakis, F. Fresi, G. Bernini, H. Avramopoulos, E. Varvarigos, and P. Castoldi, “Experimental demonstration of a fully disaggregated and automated white box comprised of different types of transponders and monitors,” _Journal of Lightwave Technology_ , vol. 37, no. 3, pp. 824–830, 2019.
  * [11] N. Sambo, K. Christodoulopoulos, N. Argyris, P. Giardina, C. Delezoide, D. Roccato, A. Percelsi, R. Morro, A. Sgambelluri, A. Kretsis, G. Kanakis, G. Bernini, E. Varvarigos, and P. Castoldi, “Field trial: Demonstrating automatic reconfiguration of optical networks based on finite state machine,” _Journal of Lightwave Technology_ , vol. 37, no. 16, pp. 4090–4097, 2019.
  * [12] A. Sgambelluri, A. Giorgetti, D. Scano, F. Cugini, and F. Paolucci, “Openconfig and openroadm automation of operational modes in disaggregated optical networks,” _IEEE Access_ , vol. 8, pp. 190 094–190 107, 2020.
  * [13] L. Velasco, A. Sgambelluri, R. Casellas, L. Gifre, J.-L. Izquierdo-Zaragoza, F. Fresi, F. Paolucci, R. Martínez, and E. Riccardi, “Building autonomic optical whitebox-based networks,” _Journal of Lightwave Technology_ , vol. 36, no. 15, pp. 3097–3104, 2018.
  * [14] J. Kundrat, J. Vojtech, P. Skoda, R. Vohnout, J. Radil, and O. Havlis, “YANG/NETCONF ROADM: Evolving open DWDM Toward SDN applications,” _Journal of Lightwave Technology_ , vol. 36, no. 15, pp. 3105–3114, 2018\.
  * [15] L. Nadal, J. M. Fabrega, M. S. Moreolo, F. J. Vilchez, R. Casellas, R. Munoz, R. Vilalta, and R. Martinez, “Programmable disaggregated multi-dimensional s-bvt as an enabler for high capacity optical metro networks,” _Journal of Optical Communications and Networking_ , vol. 13, no. 6, pp. C31–C40, 2021\.
  * [16] F. Paolucci, A. Sgambelluri, F. Cugini, and P. Castoldi, “Network telemetry streaming services in sdn-based disaggregated optical networks,” _Journal of Lightwave Technology_ , vol. 36, no. 15, pp. 3142–3149, 2018\.
  * [17] A. Campanella, H. Okui, A. Mayoral, D. Kashiwa, O. G. de Dios, D. Verchere, Q. Pham Van, A. Giorgetti, R. Casellas, R. Morro, and L. Ong, “Odtn: Open disaggregated transport network. discovery and control of a disaggregated optical network through open source software and open apis,” in _2019 Optical Fiber Communications Conference and Exhibition (OFC)_ , 2019, pp. 1–3.
  * [18] M. Filer, M. Cantono, A. Ferrari, G. Grammel, G. Galimberti, and V. Curri, “Multi-vendor experimental validation of an open source qot estimator for optical networks,” _Journal of Lightwave Technology_ , vol. 36, no. 15, pp. 3073–3082, 2018.
  * [19] “https://github.com/telecominfraproject/oopt-gnpy.”
  * [20] E. Virgillito, R.-P. Braun, D. Breuer, A. Gladisch, V. Curri, and G. Grammel, “Testing TIP open source solutions in deployed optical networks,” in _2021 Optical Fiber Communications Conference and Exhibition (OFC)_ , 2021, pp. 1–3.
  * [21] V. Curri, “Gnpy model of the physical layer for open and disaggregated optical networking,” _Journal of Optical Communications and Networking_ , vol. 14, no. 6, pp. C92–C104, 2022.
  * [22] G. Borraccini, S. Straullu, A. Giorgetti, R. D’Ingillo, D. Scano, A. D’Amico, E. Virgillito, A. Nespola, N. Sambo, F. Cugini _et al._ , “Qot-driven optical control and data plane in multi-vendor disaggregated networks,” in _Optical Fiber Communication Conference_. Optica Publishing Group, 2022, pp. M4F–5.
  * [23] G. Borraccini, R. Ambrosone, A. Giorgetti, S. Straullu, F. Aquilino, E. Virgillito, A. D’Amico, R. D’Ingillo, N. Sambo, F. Cugini, and V. Curri, “Disaggregated optical network orchestration based on the physical layer digital twin,” in _Optical Fiber Communication Conference (submitted)_ , 2023.
  * [24] “https://opennetworking.org/onos/.”
  * [25] A. Giorgetti, A. Sgambelluri, R. Casellas, R. Morro, A. Campanella, and P. Castoldi, “Control of open and disaggregated transport networks using the open network operating system (onos),” _Journal of Optical Communications and Networking_ , vol. 12, no. 2, pp. A171–A181, 2020.
  * [26] G. Borraccini, A. D’Amico, S. Straullu, A. Nespola, S. Piciaccia, A. Tanzi, G. Galimberti, S. Bottacchi, S. Swail, and V. Curri, “Cognitive and autonomous qot-driven optical line controller,” _Journal of Optical Communications and Networking_ , vol. 13, no. 10, pp. E23–E31, 2021.
  * [27] Q. Pham-Van, V. López, A. M. Lopez-de Lerma, R. Szwedowski, K. Mrówka, S. Auer, H.-T. Thieu, Q.-H. Tran, D. Verchere, G. Atkinson _et al._ , “Demonstration of alarm correlation in partially disaggregated optical networks,” in _Optical Fiber Communication Conference_. Optica Publishing Group, 2020, pp. M3Z–6.
  * [28] A. D’Amico, S. Straullu, G. Borraccini, E. London, S. Bottacchi, S. Piciaccia, A. Tanzi, A. Nespola, G. Galimberti, S. Swail, and V. Curri, “Enhancing lightpath qot computation with machine learning in partially disaggregated optical networks,” _IEEE Open Journal of the Communications Society_ , vol. 2, pp. 564–574, 2021.
  * [29] Y. Pointurier, “Design of low-margin optical networks,” _Journal of Optical Communications and Networking_ , vol. 9, no. 1, pp. A9–A17, 2017.
  * [30] G. S. Zervas and D. Simeonidou, “Cognitive optical networks: Need, requirements and architecture,” in _2010 12th International Conference on Transparent Optical Networks_. IEEE, 2010, pp. 1–4.
  * [31] W. Wei, C. Wang, and J. Yu, “Cognitive optical networks: key drivers, enabling techniques, and adaptive bandwidth services,” _IEEE Communications magazine_ , vol. 50, no. 1, pp. 106–113, 2012.
  * [32] TIP, “Open optical & packet transport project,” Tech. Rep. [Online]. Available: https://telecominfraproject.com/oopt/
  * [33] G. Borraccini, S. Straullu, A. Ferrari, E. Virgillito, S. Bottacchi, S. Swail, S. Piciaccia, G. Galimberti, G. Grammel, and V. Curri, “Using qot-e for open line controlling and modulation format deployment: an experimental proof of concept,” in _2020 European Conference on Optical Communications (ECOC)_. IEEE, 2020, pp. 1–4.