11institutetext: Carmen Mas-Machuca 22institutetext: Technical University of
Munich (TUM), Arcisstraße 21, 80333 München 22email<EMAIL_ADDRESS>33institutetext: Lena Wosinska 44institutetext: Chalmers University of
Technology, 41296 Gothenburg, Sweden 44email<EMAIL_ADDRESS>55institutetext: Marco Ruffini 66institutetext: CONNECT centre, School of
Computer Science and Statistics, Trinity College Dublin, Ireland 66email:
<EMAIL_ADDRESS>77institutetext: Jiajia Chen 88institutetext: Chalmers
University of Technology, 41296 Gothenburg, Sweden 88email:
<EMAIL_ADDRESS>
# Optical Networks and Interconnects

Carmen Mas-Machuca corresponding author Lena Wosinska  Marco Ruffini  and
Jiajia Chen

###### Abstract

The rapid evolution of communication technologies such as 5G and beyond, rely
on optical networks to support the challenging and ambitious requirements that
include both capacity and reliability. This chapter begins by giving an
overview of the evolution of optical access networks, focusing on Passive
Optical Networks (PONs). The development of the different PON standards and
requirements aiming at longer reach, higher client count and delivered
bandwidth are presented. PON virtualization is also introduced as the
flexibility enabler. Triggered by the increase of bandwidth supported by
access and aggregation network segments, core networks have also evolved, as
presented in the second part of the chapter. Scaling the physical
infrastructure requires high investment and hence, operators are considering
alternatives to optimize the use of the existing capacity. This chapter
introduces different planning problems such as Routing and Spectrum Assignment
problems, placement problems for regenerators and wavelength converters, and
how to offer resilience to different failures. An overview of control and
management is also provided. Moreover, motivated by the increasing importance
of data storage and data processing, this chapter also addresses different
aspects of optical data center interconnects. Data centers have become
critical infrastructure to operate any service. They are also forced to take
advantage of optical technology in order to keep up with the growing capacity
demand and power consumption. This chapter gives an overview of different
optical data center network architectures as well as some expected directions
to improve the resource utilization and increase the network capacity.

##  1 Keywords

Access networks, core networks, optical datacenters, network virtualization,
network planning, datacenter architectures, resource disaggregation, EON, LR-
PON, energy efficiency.

##  2 Introduction

The strict quality of service requirements of emerging services and the
rapidly increasing number of connected users and devices are bringing many
challenges for both the wireless/radio segment and the wired segments
interconnecting mobile base stations, which span from optical access to
aggregation and core networks. This trend is expected to accelerate in future
generations of mobile systems that will certainly pose extreme challenges to
provide the required ultra-high transmission capacity, ultra-low latency and
high reliability. Optical networks have long played a central role in
telecommunication networks, forming the fiber backbone of the Internet. Over
the years the penetration of fiber increased rapidly in order to support
ultra-high capacity, ultra-low latency, and high reliability performance
requirements of the services, provided by current and future generations of
mobile networks. Thanks to the unique properties of optical transmission,
photonic components and devices, as well the rapid development of optical
network equipment, optical networks and interconnects have evolved into
flexible network infrastructures able to support growing capacity demand and
the challenging service requirements. There is no doubt that optical networks
together with the next generations of mobile networks and the proliferation of
artificial intelligence throughout the network, will play a key role in the
future communication networks.

Moreover, all the modern services are in one way or another processed in data
centers, leading to a dramatic growth of data center traffic. This brings
serious challenges for the intra-data center interconnects in terms capacity
demand and power consumption. In this respect, photonics and optical
transmission are the only viable technologies for the intra-data center
interconnects.

This chapter will guide the reader through different segments of optical
networks and interconnects, providing an overview of their developments and
future trends. The chapter is divided in four parts. The first section is
dedicated to optical access networks, which are crucial for the development of
new generations of mobile networks and services. The second part is focusing
on optical core networks and addresses various network planning problems, as
well as optical network control and management. The third part is
concentrating on optical intra-data center interconnects, describing possible
architectures, highlighting the main challenges and future developments.
Finally, concluding remarks are given in the fourth part.

##  3 Optical Access Networks

This part provides an overview on the architectural evolution of optical
access networks, focusing on Passive Optical Networks (PONs). This section
starts by describing the evolution in PON standards. Then it delves into
architectural evolution, proposed in the research literature, to improve
capacity, cost effectiveness and support for new services, namely ultra dense
WDM, Long-Reach PON and mesh PON architectures (some time referred to as EAST-
WEST or inter-Optical Network Units (ONUs)). It then describes the current
trend of PON virtualisation, emphasising the increase in flexibility that this
can provide.

###  3.1 Overview of optical access networks architecture evolution

#### Evolution of classical PON architectures

The concept of bringing fibre connectivity all the way to the home (i.e.,
Fibre to the Home - FTTH) has been around for several decades, with first
trials appearing as far back as 1977 in Japan FTTH_77 . It was soon realised
that scalability was going to be the main issue, as access technology needs to
scale to tens or hundreds of million connections for each operator. Network
infrastructure sharing was identified as a viable option for reducing cost,
especially as it suited well the highly statistical nature of end user
capacity demand and utilisation. This led to the development of the Passive
Optical Network (PON) concept, where infrastructure sharing is achieved
through the use of passive power splitters, thus creating a broadcast and
select type of architecture. The low loss and high capacity of optical fibre
transmission was a key enabler for such passive split system.

Over the years, PON technology and standardisation have evolved, providing
increasingly larger capacity, which was required to support increasing
bandwidth demand from multi-media applications. Starting from the Telephone
PON (T-PON), offering the equivalent of 128 ISDN (144 Kb/s) connections from a
single feeder fibre, ITU-T standards have evolved towards Broadband PON
(B-PON), offering 155Mb/s upstream and 622 Mb/s downstream, Gigabit PON
(G-PON) offering 1.25 Gb/s upstream and 2.5 Gb/s downstream and XG-PON (i.e.,
10G-PON, 2.5 Gb/s upstream and 10 Gb/s downstream). The rates provided here in
brackets were the most widely used, as standards typically allow a range of
possible transmission rates. In addition, all such technologies are based on
Time Division Multiplexing (TDM), and often referred to as TDM-PON systems. It
should be noticed that some of the standards, in particular (G-PON and XG-PON)
have offered asymmetric upstream/downstream rates. Unlike copper-based
Asymmetric Digital Subscriber Line (ADSL), where such asymmetry was due to the
limited bandwidth of the medium, for PONs this was due to the higher
complexity of the Optical Line Terminal (OLT) receiver, which needs to operate
in burst mode in order to enable upstream multiplexing of burst data
transmission from the end user Optical Network Units (ONUs).

After XG-PON, standardisation moved towards multi-wavelength solutions, with
NG-PON2 (Next-Generation PON 2), offering four Dense Wavelength Division
Multiplexing (DWDM) channels with symmetric 10 Gb/s capacity, hence the name
of Time-Wavelength Division Multiplexing PON (TWDM-PON). In practice though,
NG-PON2 saw little deployment due to the high cost of the ONUs (which required
tunable laser and filters). For this reason a new standard XGS-PON (10G
Symmetric PON) was developed to bring a symmetric 10Gb/s PON, with an upgrade
path towards the multi-wavelength NG-PON2 system. In the meantime the
evolution towards higher data rate channels has progressed, with ITU-T HS-PON
(High-Speed PON) working group, providing 25 Gb/s upstream and 25 to 50 Gb/s
(still under investigation) downstream.

Another standardisation body, the IEEE, has also defined PON standards (the
E-PON - Ethernet PON family), built upon the Ethernet standard. IEEE and ITU-T
PON standards are similar in architectures and data rate evolution, with the
ITU-T providing additional quality of service management features in the
upstream.

In parallel with these standardisation efforts, other architectures have been
proposed and implemented through proprietary solutions, such as Wavelength
Division Multiplexing (WDM) PON. The main difference with respect to TDM PON
is the use of a wavelength multiplexer instead of the power splitter (although
mixed use of multiplexer and splitter is also found in some architectures).
The idea is to provide one dedicated wavelength channel to each end user: thus
fibre infrastructure is shared through WDM, but each user has a dedicated OLT
termination. This concept was further developed in proposals such as Ultra-
Dense WDM PON (UD-WDM-PON) UDWDM , where coherent technology was developed to
provide in the order of a thousand different channels. The main reason
hindering its development was similar to that for NG-PON2, i.e., the high cost
of tunable transceivers, in addition to requiring one OLT device for each end
user. While this can potentially offer high-capacity dedicated channels to end
users, it should be noticed that computer networks are always built upon
statistical multiplexing principles, in order to remain financially viable and
scalable. Thus, while WDM-PON can provide large uncontended capacity to each
end user, such capacity is multiplexed at the central office (CO), where
contention inevitably occurs. The advantage of TDM (or TWDM)-PONs is that
statistical multiplexing is carried out closer to the end users, improving
resource efficiency. At the same time, the broadcast nature of the power split
architecture enables the overlay of dedicated wavelength connections for
specific users, for example if a business requires a dedicated 25G connection
it will bear the extra cost for a more complex ONU and a dedicated OLT.

#### Long-Reach PON architectures

An interesting architecture that has been considered for long time (it was
initially proposed in 1990 Hill_90 ), is the Long-Reach PON (LR-PON) LR-PON .
It’s distinctive feature is that it adopts a much longer optical reach and
split ratio (i.e., number of ONUs) than conventional PON architectures. The
main rationale behind the LR-PON concept is that today’s network topology,
with COs often within a few km of any end user, was driven by the reach
limitation of the copper transmission technology when cables were laid out
over a hundred years ago. The concept behind LR-PON is to avoid using fibre
just as a mere replacement for the transmission medium, and instead use its
extraordinary transmission capability to provide a complete redesign of the
network architecture, thus delivering more cost effective increase of overall
network capacity. The key idea is to exploit the low transmission loss, high
bandwidth and low cost amplification of optical technology to design PONs able
to cover transmission distances above 100 km and split ratios up to 1024.

This architecture brings about two main changes in the network. It enables
central office consolidation, reducing their number, typically by almost two
orders of magnitude. It simplifies the core network, as the reduction in
number of COs brings about the possibility to provide a flat, single tier core
network (i.e., without electronic hops) connecting all COs through a mesh
topology of wavelength channels Flat-core . Two main LR-PON architectures are
shown in Figure 1. One providing a long feeder fibre (90 km), followed by a
short (10 km) Optical Distribution Network (ODN). This is shown on the left
hand side of Figure 1, where the first stage splitter provides optical
amplification and two additional splitter locations further distribute the
signal, typically following existing duct routes. Another architecture, more
suitable for lower density scenarios (i.e., rural areas) is the ring feeder,
shown on the righ hand side of Figure 1, where multiple COs are connected
through a feeder ring.

Figure 1: Two different LR-PON architectures. On the left hand side, a tree-
based topology with long feeder fibre and 10 km ODN; on the right hand side a
ring-based topology where splitter nodes are distributed across a wide area.

As mentioned above, this enables a simpler core network, where the only
electronic packet switching is carried out at the COs, with no need for
intermediate electronic packet routing within the network domain. Figure 2
shows how the number of COs could be reduced by two orders of magnitude, for a
scenario built around Italy’s national network, moving form over 10,000 COs,
down to 116 nodes.

Figure 2: LR-PON central office consolidation for the Italian network use
case.

The diversity of this architecture also enables novel approaches to its
control and operation. For example, the larger geographic span of each CO (now
covering entire metropolitan areas) can facilitate higher infrastructure
sharing for protection purposes, as proposed in Prot1  and demonstrated in
Prot2 .

A key element for the cost-effectiveness of the LR-PON architecture shown in
Figure 1, which enables the long-reach feature, is the amplification at remote
nodes (i.e., the first stage splitter). Here an optical amplifier (typically
an Erbium Doped Fibre Amplifier- EDFA, but Semiconductor Optical Amplifiers -
SOAs can also be adopted, as shown in LRPON-SOA ). Just two amplifiers (one
for each direction) can provide enough gain for multiple wavelength channels,
(40 bidirectional channels were demonstrated in LRPON-demo  operating in the C
band). Taking in consideration the tree-based architecture in Figure 1, for
example, the power budget can be of the order of 25 dB for the feeder, which
is compensated by the amplifier, before entering the ODN section, where a
split of 1024 and a reach of 10 km are supported with a power budget of 30 dB.

One of the main drawbacks of this architecture, which has hindered its
practical development, is that it provides a level of centralisation that is
in antithesis to the latest trend of edge cloud computing, which requires
active networking and computing nodes close to the end user. This is driven by
both 5G application requirements (i.e., ultra-low latency, which often require
use of local edge nodes for processing) and 5G networking requirements, where
low latency is required to support cloud Radio Access Networks (C-RAN),
functional split architectures NGMN .

#### PON evolution in support of small cells and edge computing

One of the major approaches of 5G to capacity increase is Radio Access Network
(RAN) densification, through dense deployment of small cells. While macro
cells and other computing or service nodes have been typically connected by
point-to-point fibre links, as 5G increases their number by orders of
magnitude, point-to-point fibre solutions become too expensive. Recently, PONs
have been considered as a more viable option to connect 5G small cells and
Multi-Access Edge Computing (MEC) nodes PON_backhaul . A key advantage of PONs
is that they are cost effective by means of providing statistical
multiplexing. As small cells cover smaller areas and thus a smaller number of
users at a time compared to larger macro cells, their load will fluctuate more
over time, making them a good match for TDM (or TWDM) PONs, which provide
statistically multiplexing ability. However, since PONs were initially
designed to offer broadband connectivity to residential users and small
businesses, some work is needed to improve their performance to match the 5G
network requirements, in terms of latency, jitter and capacity.

From a capacity perspective, as mentioned above, current standards will soon
be delivering 50Gb/s, while higher rates of 100 Gb/s and beyond are being
investigated. In addition, multi-wavelength systems are envisaged to be a
necessity if PONs are to provide fronthaul RAN transport services. While
multi-wavelength systems proved to be too expensive for residential services,
their cost can be justified for 5G small cell, especially considering the fact
that the alternative of leasing point to point fibre can be much more
expensive. Another issue is that current PONs cannot easily deliver latency
below 1 ms. When considering fronthaul transport, for example the popular
OpenRAN 7.2 split, latency requirements are of the order of 100 $\mu s$
between the Remote Unit (RU), where the antenna is located, and the
Distributed Unit (DU), where the signal baseband processing is located ORAN-
latency . A solution to this problem was initially proposed in NTT-OFC-2014 ,
called Cooperative DBA, where it was recognised that the fronthaul latency
issue could be eliminated if both RAN and PON schedulers could be
synchronised. The difference in operation compared to a standard PON is shown
in Figure 3. The left hand of the figure shows a typical PON DBA operation,
where the ONU needs to transmit a report indicating its upstream queue level
to the OLT, so that capacity can be scheduled for the next cycle. This
operation is time consuming, so if the ONU serves a small cell, the incoming
packet needs to wait in the queue until this process is completed and a
transmission opportunity is granted. In Cooperative DBA, shown on the right
hand side of the figure, the small cell scheduler provides advanced
information on when the end user (i.e., the handset) is scheduled for upstream
transmission, so that the time such data will arrive at the ONU can be
accurately estimated. This enables the OLT to calculate the bandwidth map in
advance and thus provide the ONU with an upstream transmission opportunity a
few $\mu s$ after the arrival of the packet from the small cell (which is
directly connected to the ONU). This concept was recently standardised at the
ITU-T as Cooperative Transport Interface (CTI) CTI .

Figure 3: Standard DBA operation for a PON fronthaul application (left) and
Cooperative DBA solution for coordinating fixed and mobile scheduler (right).

Another aspect of 5G evolution is that of edge computing: the high capacity
and low latency demand posed by 5G network services and applications has
driven the development of the edge cloud, i.e., moving computing capacity
closer to the end user. There is a multitude of envisaged scenarios, for
example: computation offload from mobile handset (and smart glasses in the
near future) for applications such as real time object recognition; support
for autonomous driving and smart traffic intersections; support of cloud-RAN
services in high densification scenarios, etc. While edge computing nodes can
provide such localised computing facilities, the issue remains on how to
interconnect them with the rest of the access network. Due to their envisaged
high density (which could be similar to the number of small cells in a 5G
access network), and their mesh traffic patterns (i.e., MEC nodes will need to
communicate with multiple 5G cells, towards COs and to other MEC nodes, as
shown in Figure 4), providing cost effective connectivity remains a major
challenge.

If the MEC architecture has a hierarchical structure that can be mapped into a
tree topology, for example a given MEC serving a number of small cells, then
overlapping PONs could be considered. For example, a first-stage PON connects
the CO to a number of splitters, some of which could host MEC nodes, which, in
turn, could operate a second-stage PON network connecting to the small cells.
However this solution is static and prescriptive, i.e., it does not allow an
MEC node to support other small cells (i.e., for load balancing), or to other
MEC nodes (i.e., for sharing information at lower latency, with respect of
being routed through a CO).

Figure 4: The introduction of MEC nodes in the network changes the traffic
patterns into a mesh demand matrix.

Other solutions are based on more flexible architectures that enable direct
communications across end points Intra-ONU1 , Intra-ONU2 . Figure 5, shows a
solution based on the use of tunable Fibre Bragg Gratings MESH-PON , to
selectively reflect wavelength channels towards the end points, thus enabling
direct communication between them. The idea is that an OLT can be located at
an end point and one of its wavelengths be reflected back by one of the
splitters towards the other ONUs, creating a local communication group. By
selecting which wavelength to let through and which to reflect, at each
splitter, multiple such local communications groups can be created, even
across different PON branches MESH-experiment . This type of architecture
effectively adds the EAST-WEST direction of communication to a PON, in
addition to the classical NORTH-SOUTH, thus enabling mesh connectivity between
MEC nodes and towards small cells or other end points. One of the issues of
this model however, is that as the signal traverses the splitters more than
once, they get attenuated. Thus, connecting end points across multiple
branches will likely require optical signal amplification (i.e. EDFAs), as
they will traverse multiple splitters.

Figure 5: Mesh-PON architecture enabling direct communicaiton across end
points, through wavelength reflection at splitter location.

###  3.2 Optical access network virtualisation

The emergence of 5G was accompanied by another major revolution in networking,
the virtualisation of network functions, known as NFV (Network Function
Virtualisation). While this is a vast topic that involves a large pool of
technologies, from data centres to telecommunications networks, this section
briefly discusses how virtualisation has affected the optical access network
domain Cloud-access . While the use of virtualisation was applied to PCs and
servers for a long time, the concept has reached the networking realm
relatively recently. This occurred through NFV, where network functionalities
typically implemented in proprietary equipment, have moved to run as software
instances over general purpose servers. In telecommunications networks, the
key milestone occurred when virtualisation reached the Central Office. The
first project to go towards this direction was the Central Office
Rearchitected as a Data Centre (CORD) CORD , which aimed at running services
in a Telecomms central office as open source software on general purpose
servers and whitebox switches and routers (i.e., open systems based on
merchant silicon).

There are several reasons behind this move towards virtualised systems. Some
of the main advantages are:

  * •

encourage a competitive environment, which typically leads to cost reduction.

  * •

increase interoperability among systems, leading to increase in
functionalities and avoidance of vendor lock in.

  * •

provide a higher degree of programmability, leading to increased
customisation.

  * •

enable slicing, leading to better support for multi-tenant and multi-service
environments.

The CORD architecture was initially partitioned into three use cases:
Residential (R-CORD), Mobile (M-CORD) and Enterprise (E-CORD). Over time these
have evolved and delivered new projects. For example, the SDN enabled
broadband access (SEBA) is a lightweight variant of R-CORD, supporting fixed
broadband technologies such as PON, G.Fast (delivering fibre to the cabinet)
and DOCSIS (broadband over coaxial cable). The Converged Multi-Access and Core
(COMAC) project was developed to integrate mobile and fixed access with the
network core. AETHER is a new platform extending the CORD concept to support
edge cloud as well as private 5G connectivity.

This section focuses on the virtualisation aspects related to PONs. While the
SEBA project provides the framework required to run a fixed access network,
the Virtual OLT Hardware Abstraction (VOLTHA), provides the control and
management aspect of a PON access system. VOLTHA’s northbound interfaces
allows the PON OLT to be abstracted as an Ethernet switch to the network
controller (for example the ONOS controller developed by the Open Networking
Foundation -ONS). The southbound interface instead connects directly to the
PON whitebox device. This virtualisation enables the creation of PON slices,
where different tenants can share the same PON infrastructure with the total
PON capacity shared across multiple PON slices. The system has been
successfully demonstrated for operation in typical residential PON use cases,
including production deployment by the German operator DT DT-SEBA .

#### Deep PON virtualisation

While the abstraction level provided by VOLTHA and SEBA addresses the current
needs of residential PON customers well, 5G is bringing about new
requirements. As mentioned above, PONs are being considered as a solution to
reduce the cost of mobile small cell densification, which uses functional
split architectures. This makes use of lightweight remote antenna devices
called Remote Unit (RU), connected via fibre to edge compute nodes or central
offices, where the rest of the digital signal processing is carried out in
Distributed Units (DU) and Centralised Units (CU) NGMN . While this
architecture provides the benefit of computing centralisation, as only a
minimum amount of processing is carried out at the RU site, it poses strict
requirements to the interface between the RU and DU, in particular, it
requires a latency of the order of 100 $\mu s$. Since the RU is located at the
very edge of the network, PON connectivity, where already deployed for other
residential uses, can provide high capacity and reliable connectivity at low
cost. However, the latency requirement of the RU-DU interface is not supported
by standard PONs, as the upstream scheduling mechanism typically operates on
the timescale of a few ms. As mentioned above, the Cooperative Transport
Interface provides a solution to this problem, and virtualisation of the PON
scheduler, which goes beyond the VOLTHA implementation, can facilitate its
implementation. An example of PON scheduler implementation was proposed in the
FASA architecture FASA , standardised in BBF-402 . However, operating in a
multi-tenant and multi-service environment, i.e., sharing a physical PON
across multiple mobile and residential services, requires each tenant to
control a scheduler instance.

This concept was developed in vDBA-concept , and standardised in BBF-402 ,
BBF-370 , with the name of virtual Dynamic Bandwidth Allocation (vDBA), where
each different tenant (or service) can operate an independent upstream
scheduler (DBA) allocating a capacity up to a given maximum level. Following
the example in Figure 6, each vDBA would calculate an independent upstream
scheduling allocation (called Bandwidth Map - BWMap) and send it to a merging
engine (ME), which has the task of merging all upcoming BWMaps, while
resolving any collisions in the allocations.

Figure 6: Virtual DBA mechanism for multi service and multi tenant operation.
Each Virtual network operator (VNO) can calculate an independent bandwidth map
allocation, which then is merged into a common physical bandwidth map that is
sent to all ONUs.

The merging engine thus becomes the critical element for supporting QoS.
Initial approaches to the ME have followed relative prioritisation, typical of
standard stateless Ethernet/IP queue management approaches, where packets from
services with higher priority are given preference over packets from lower
priority services vDBA-test . The issue with simple packet prioritisation is
that it is based on relative importance between flows. However, this does not
operate over the key performance metrics of a service level agreements (SLA),
such as latency and jitter, thus it acts blindly and can be difficult to
control, especially with respect to flows that have similar level of priority.
A solution was proposed in OFC-21 , where a ME was designed to operate
directly on SLA parameters for next-generation services, for example that a
given latency of 100 $\mu s$ is maintained for 99% of upstream slot
allocations, measured over a given time window. An example of a second SLA-
oriented service could require a latency of 50 $\mu s$, but with 95%
availability. If relative prioritisation was to be used, it would be difficult
to determine which of the two services should be given higher relative
priority, so the key approach here is to act on the actual SLA parameters. The
solution proposed in OFC-21  requires the use of a stateful mechanism, meaning
that the algorithm that implements the merging process keeps track of the
current and past performance (the state) of all services that require QoS
(i.e., SLA-aware). Any time there is a scheduling collision between two DBA
allocations, the algorithm checks the flow delivery statistics and gives
priority to the one that is closer to breaching its SLA. The effect of this
algorithm is to minimise the chance for any service to break its SLA, which is
the main concern that a PON operator would have in providing SLA-oriented 5G
services. In this way, an operator could carry out simulation analysis for
different traffic profiles and SLA types to estimate the maximum number and
types of services the PON can support before leading to SLA breach.

##  4 Optical Core Networks

Telecommunication networks are commonly categorized into three segments: core,
aggregation and access networks. Core networks are the most capacity hungry
segment as its demands are between Internet Exchange Points (IXPs), data
centers and big cities. The most common topology of core networks is bi-
connected mesh topology, which allows interconnecting any two nodes with at
least two disjoint paths. This requirement allows operators to increase the
robustness of their network in case of single failures. Indeed, this
requirement is associated with the high capacity of optical links.

The first optical core networks were opaque, that is, the signal transmission
was optical, whereas the switching was performed in the electrical domain.
Converting the signal to the electrical domain at each node required a large
investment due to the high number of transceivers. In order to reduce the
cost, translucent or even transparent optical core networks have been
implemented such that not only the transmission but also the switching is
performed in the optical domain. Transparent optical networks not only reduce
the number of required transceivers, but also the consumed power and the
control data to configure the lightpath.

Figure 7: Opaque (left) vs. Transparent (right) optical networks Achim2011

Optical links consist of optical cables with tens or hundreds of fibers. The
capacity of each fiber depends on the multiplexing technique. In core
networks, Wavelength Division Multiplexing (WDM) is used to increase the
capacity according to the number of wavelengths (e.g., Dense WDM (DWDM)
supports 40 channels at 100 GHz spacing or 80 channels with 50 GHz spacing,
whereas Ultra Dense WDM (UDWDM) supports hundreds of channels with 12,5 GHz
spacing Li2018 ). As the link distances in core networks are large (hundreds
of kilometers), amplification is required, resulting in heterogeneous span
lengths from 40 to 120 km Chin2017 . Furthermore, the node components have
also evolved: from opaque nodes performing the switching in the electrical
domain, towards transparent optical Reconfigurable Optical Add/Drop
Multiplexers (ROADMs). Current ROADMs have the following properties: Colorless
(i.e., any color can be added/dropped at any port), Directionless (i.e., any
color can be sent to any fiber/direction) and Contentionless (i.e., any color
can be added/dropped in different directions simultaneously).

Triggered by the high bitrate of the demands in core networks, Bandwidth
Variable Transponders (BVT) allow setting lightpahts with a range of bitrates
from 100 to 600 Gb/s Sai2020 . Based on the lightpath characteristics (length,
bitrate, etc.) the BVT will have to be configured accordingly in terms of
modulation format, required spectrum, etc.

Network operators are currently using the C-Band spectrum for their demands.
However, when using the ITU fixed-grid, the number of lightpahts that can be
set and the spectrum utilization are limited. In order to overcome these
limitations, flex-grid combined with the use of BVTs, allow channels to use
the spectrum they need according to their requirements (e.g., bitrate) while
reducing wasted spectrum. In the next section will address the related
planning problems more in detail.

Core operators are considering other possibilities to increase the capacity of
their networks (refer to Figure 8: i) Lighting dark fibers (LDF) which uses
the available dark fiber by lighting them with BVTs as needed; (ii) Band
Division Multiplexing (BDM) uses other bands (e.g., C+L) in order to have
broader spectrum although some equipment may be replaced or added to be
compatible with the new bands; (iii) Multicore Fibers (MCF) replace the
capacity of several SMF with one MCF.

Figure 8: Different alternatives to increase the capacity of a link: LDF, BDM,
MCF

###  4.1 Network planning problems

#### Routing and Spectrum Assignment problems

The Routing and Wavelength Assignment (RWA) problem of WDM based networks
without wavelength conversion, aims at finding the appropriate path and
wavelength for a particular demand (defined by a required bitrate from a
source node to a destination node). This problem has two constraints: i) the
wavelength continuity constraint, which requires that the same wavelength can
be used along all the links of the path; and ii) the wavelength assigned to
one demand cannot be assigned to a second demand, which shares at least one
link. These concepts will be shown with the example depicted in Figure 9(a)
where three nodes ($A,B$ and $C$) are interconnected through links $l_{1}$ and
$l_{2}$. For simplicity, let’s consider 4 fixed grid slots of each link, two
of them used for other demands with different bitrates. If a demand has to be
set from $A$ to $C$ through $B$, the demand can be assigned to the last slot
so that the two constraints can be guaranteed (as depicted in green in Figure
9(b)).

With the development of flexible and configurable transponders, and the
possibility to use the spectrum more flexibly and according to the demand’s
requirements, lightpath planning has evolved towards the Routing,
Configuration, and Spectrum Assignment (RCSA) problem. The development of
transponders supports software tunable channel configurations. Each
configuration is associated to a particular combination of data rate,
modulation format and FEC. Given a demand and a path, each configuration
corresponds to a particular number of frequency slots to guarantee the minimum
Optical Signal to Noise Ratio (OSNR) at the receiver. In elastic optical
networks, where the spectrum is divided into smaller slots, the spectrum
required by each demand should be mapped into a certain number of slots for
all the links along the path. The RCSA problem aims at finding the appropriate
path, configuration and spectrum for a particular demand. The problem has
three constraints: i) the spectrum continuity constraint, which requires that
the same spectrum slots are used along all the links of the path; ii) the
spectrum slots assigned to one demand cannot be assigned to a second demand,
which shares at least one link, and iii) the spectrum contiguity constraint,
which requires that the spectrum slots are contiguous Sai2020 ; Amir2021 .
Let’s consider the example shown in Figure 9(a) where the low bit-rate demand
from $A$ to $C$ should be set given the available spectrum. As the spectrum
used for the demands is narrowed down to the required one based on the
configuration, the new demand could be allocated between orange and light blue
demands (shown in green in Figure 9(c)), leaving more contiguous available
spectrum for future demands (e.g., the yellow demand with high bit rate as
shown in Figure 9(c), which was not possible to be allocated in the Fixed Grid
example of Figure 9(b)) .

Figure 9: Example of setting a demand from $A$ to $C$ through $b$ given the
available spectrum shown in (a), when using (b) RWA and (c) RCSA

#### Resilience

Resilience is one of the most important requirements of optical core networks.
As mentioned earlier, core networks have their nodes at operator locations,
that are continuously monitored (e.g., temperature, humidity) and maintained.
However, links are installed along roads, train rails, etc. and are more
likely to fail. Link failure rates range between 0.1 to 2.1 failures per 1,000
km per year OFReport , which means that a core network such as the European
COST266 SNDlib  with 42,436 km, is expected to have tens of link failures per
year. Those links support demands of hundreds of Gb/s, which means that if the
link fails (considering the number of fibers and the number of channels per
fiber when using xWDM techniques), the interrupted traffic will be of the
order of Pb/s.

Fault management of optical networks consists of the following processes:

  * •

Failure detection: The performance monitoring of optical networks relies on
the data monitored at the transceivers, as well as the monitored information
from any controlled component and any signal monitoring (e.g., based on signal
tapping). Kilper2004 ; Mrozek2018 . The performance monitoring of elastic
optical networks is more challenging due to the complexity of the new
components and the sensitivity to the physical impairments in order to
efficiently use all the spectrum. The large amount of monitored data (e.g.,
one transceiver may have 10 monitored parameters every 30 seconds Isabella2021
) enhances the problem complexity. Machine learning techniques are suitable to
cope with such large amount of data and detect anomalies in a fast and
accurate manner Shahkarami2018 ; Faisal2018 . The accuracy is measured based
on the false positive and false negative rates.

  * •

Failure identification/localization: Once an anomaly/failure is detected, the
exact identification of the type of failure and its location should be
determined. For that purpose, different techniques such as machine learning
Panayiotou2018 ; Isabella2021  or heuristics Vela2018  have been proposed.

  * •

Failure reparation: Once the failure has been identified, the reparation
process is triggered, which depends on many parameters such as priority,
available technicians and spare components, failure location, etc.
Nevertheless, the operator is also concerned about the number and category of
the interrupted demands. The SLAs signed with the clients, include a maximum
demand unavailability SLA . When this value is exceeded, the operator should
pay penalties which increase with the demand interruption time. Furthermore,
the fierce competition encourages operators to comply with the client’s
requirements and keep their clients as long as possible.

The objective of operators is to use their network resources efficiently while
coping with the demand’s requirements. Given a demand with a minimum
availability, the operator should select the resources that will be assigned
to that demand. There are two main categories of resilience schemes:
protection and restoration. The former reserves some backup resources for a
demand before any failure occurs. These backup resources may be dedicated to a
particular demand (higher availability but high resource redundancy), or
shared with other demands (lower availability in case of multiple failures but
lower redundancy) Shen2014 ; Liu2013 . Another technique to offer protection
in mesh topologies aiming to reduce the spare resources are p-cycles, which
consists of finding cycles offering protection in case of link failures (one
of those cycles can cope with any on-cycle and straddling link failures).
p-cycles offer ring like recovery speed and mesh-like capacity efficiency and
they have been shown to reduce significantly the resource redundancy can also
be easily applied to optical networks Schupke2002 ; Ji2013 .

In contrast to protection that has to be computed before the failure event,
restoration is triggered once the failure(s) has/have been identified. Based
on the available resources, the interrupted demands are aimed to be restored
Liu2015 . Although restoration requires lower resource redundancy, it has
longer interruption time as it can be triggered only once the failure(s)
has/have been identified. Some works have addressed these schemes not only at
the optical layer but at higher layers (e.g., IP) Papanikolaou2017 ; Chiu2011
.

#### Placement Problems

This section briefly presents some placement problems that optical network
planners have to address.

  * •

Wavelength Converter placement problem: The lightpaths configured in
transparent optical networks have the same wavelength/spectrum along the path.
This wavelength/spectrum continuity constraint increases the blocking
probability of the demands. The problem could be solved by installing
converters at all nodes, but this significantly increases the costs.
Therefore, in order to find a compromise between the number of converters and
reducing the blocking probability, the wavelength converted placement problem
has been addressed WC1 ; WC2 ; WC3 ; WC4 . The proposed solutions differ
depending on the traffic scenario (static vs. dynamic demands) and on the
methodology (tabu-search, analytical, machine learning, Integer Linear
Programming (ILP), etc.).

The objective of the wavelength converter placement problem is to minimize the
number of converters in the network given the network topology, the lightpath
demands, and the number of ports supported by the wavelength converters. The
problem finds the paths for the requested demands and the wavelength(s)
assigned to them, assuring the wavelength continuity constraint is guaranteed
unless there is a wavelength converter in the node with enough capacity(ports)
and within the conversion range WC3 .

  * •

Regenerator placement problem: Optical regenerators allow overcoming the
maximum optical reach limitation. Optical regenerators are classified into
three categories: (i) 1R regenerators are able to re-amplify the optical
signal, (ii) 2R regenerators are able to re-amplify and reshape the optical
signal, and (iii) 3R regenerators are able to re-amplify, reshape and re-time
the optical signal Boscolo2005 . Optical regenerators rely on non-linear
effects such as self-phase modulation, cross-phase modulation, and Four Wave
Mixing. However, thanks to the cost decrease and the flexibility of optical
transponders, B2B regenerators Simmons2008  are most used as they also perform
all the regenerator functions as well as wavelength conversion. These
capabilities significantly increase the network flexibility as longer
lightpaths with high bitrates can be set, which allow reducing the number of
required transponders, specially in core networks with a short diameter (e.g.,
Germany50, which has a diameter of 934km) Khomchenko2021 ; Saquib2022 . The
regeneration placement problem identifies the network nodes that should host
regenerators such that all the demands can be set. There is a second problem
referred as regenerator assignment problem, which deals with the assignment of
regenerators to the demands such that the blocking probability is zero
Cavalcante2019 ; deSilva2020 . In that case, the problem results in the
dimensioning of the regenerators at each regeneration location and therefore,
it is directly related to the network investments.

###  4.2 Optical Network Control and Management

Optical network operators provision their services, i.e., lightpahts, through
their control and management planes. The previous section has summarized
different approaches to compute the resources that have to be reserved for a
given demand. However, these resources have to be mapped to the transceiver’s
configuration and to forwarding rules at the intermediate ROADMs along the
path. The management plane deals with the Fault Management, Configuration,
Accounting, Performance and Security (FCAPS) tasks ISO . The Simple Network
Management Protocol (SNMP) deals with the data collection, data organization,
and monitoring of the different network devices. On the other hand, the
control plane deals with the functions associated with the lightpath(s)
provisioning. The control plane needs standard interfaces to operate across
domains and ensures vendor inter-operability Casellas2018 . Hence, the
functions of the control plane are: element addressing; dynamic resource
discovery; automatic topology and reachability; RSCA computation and service
provisioning with recovery (protection and restoration) schemes while ensuring
efficient resource usage Casellas2018 . The control plane can be centralized
or distributed. The former (e.g., pure Software Defined Networking (SDN)) has
a controller hosting all the functions and interacting with each of the
network components. The later (e.g., Generalised Multi-Protocol Label
Switching (GMPLS)) places a controller at each node so that a communication
between all the controllers is required in order to have a consistent network
operation. Both alternatives have advantages and disadvantages. A centralized
control plane merges all the functions in a single entity, which requires less
state synchronization but it presents a critical failure location.
Furthermore, some functions (e.g., dynamic restoration) perform better in
distributed control planes, as the state information is disseminated along the
network. However, more synchronization is required. Hence, real deployments
implement a hybrid control plane.

Furthermore, network virtualization applied to optical networks allows
isolating different slices while coping with the network resources in a
flexible manner virt . In other words, network virtualization provides
flexible network resource provisioning such that optical network operators can
allocate the optical resources to different virtual optical networks, i.e.,
clients. Each client can have the control of the assigned resources but
without being able to disturb other virtual networks, even if they are sharing
the same physical network. Abstraction is a key concept for optical network
resource provisioning, which allows hiding the physical layer information from
the resources that should be provisioned. One abstraction technique is
participation, where the resources of e.g., a switch or a fiber are sliced
into multiple parts, so that each virtual network can use a different part.
The opposite technique is aggregation, where several resource parts are merged
into one bigger virtual entity.

Figure 10: Example of two virtual optical networks (VON) on the same physical
layer

Last but not least, NFV allows replacing network functions implemented in
custom hardware appliances with softwarized functions that can be run on
different virtual machines at different servers. Some examples of NFV applied
to optical networks are: the global transport SDN proposed by the Optical
Internetworking Forum (OIF) and the Open Networking Foundation (ONF), the
control of transport networks framework proposed by Internet Engineering Task
Force (IETF), or the Path Computation Element as a Network Service
architecture proposed by IETF.

##  5 Optical Data Center Interconnects

Driven by growing importance and popularity of online services executed at
data centers (DCs) the related traffic demand is drastically increasing.

Consider for example a DC with 1,024 racks, with 20 servers in each rack, and
with 16 processor cores, 64 GB of memory, and 48 TB of storage in each server.
This corresponds to a DC infrastructure having 327,680 cores, 1.31 PB of
memory, and 983 PB of storage. Such DC infrastructures are commonly owned and
operated by companies such as Google, Microsoft, Facebook, etc., to provide
modern web and mobile applications such as social networking, search, maps,
and video streaming, to give a few examples. Besides, most of the services
handled by DCs come with very strict latency, reliability, and security
requirements. This brings serious challenges for interconnection between
servers and racks inside the DCs.

The main challenge is to provide sufficient capacity that can support the
growing intra-DC traffic volumes. Upgrading existing infrastructures with
additional electronic equipment will lead to a drastic increase of energy
consumption and latency, as well as lower reliability performance.

Large capacity demand in DC inteconnects leads to the need for deploying
optical networks inside the DCs. Only optical communication technology can
offer a sufficient capacity. However, traditional DC network architectures
limit the use of optics to the physical cables only, while optical
interconnects offer much higher transmission and switching capacity, as well
as much lower energy consumption per bit compared to the interconnects based
on the electronic packet switching.

In addition, the increasing service demand leads to a need for upgrading DCs
with more computing resources. Therefore, the efficient utilization of
computing resources in terms of central processing units (CPUs), memory and
storage units gains high importance. Currently, an inefficient utilization of
compute resources comes from the deployment of integrated servers, where the
number of CPUs, memory and storage units is pre-defined. In a big DC thousands
of blade servers are interconnected in the network. A blade server contains
fixed amount of different resources (i.e., CPU, memory, storage) integrated
together on the server’s bus. However, the services running on the servers are
diverse and may require different ratios of these resources.

The mismatch between the diversity of resource required and the fixed amount
of resources integrated in the physical blade servers may lead to so called
resource strandingDC1 . Resource stranding means that the applications running
in a server have used up one type of resource while the other types of
resources are still idle and cannot be used. Therefore, it may be beneficial
to disaggregate different resources in DCs and utilize them according to the
demand. However, it comes with tough requirements on the capacity and latency
of the network interconnecting different types of computing resources, which
can only be satisfied by photonic technologies.

To illustrate the current developments and the possible ways to address the
aforementioned challenges, this sub-chapter presents various optical DC
network architectures along with the future developments in terms of possible
solutions for energy saving, increased capacity, and resource disaggregation.

###  5.1 Optical DC network architectures

Figure11 illustrates a hierarchical DC network architecture consisting of
three levels, referred to as tiers, i.e., access (edge) tier, aggregation
tier, and core tier.

Figure 11: Hierarchical DC network architecture DC2

The servers in the DCs are grouped and mounted in racks. The access tier is
responsible for interconnecting the servers within the rack. Therefore, the
access (edge) tier is often referred to as top of the rack (ToR) switch. The
ToR switches are further connected to the higher tier. In the current DC
networks, the optics is mainly used for transmission, while switching is
performed by electronic packet switches (EPSs). One of the major issues of
such EPS-based DC network is the high power consumption. Therefore, the next
step in the evolution of the DC network architecture is reducing the number of
EPSs by merging aggregation and core tiers and by replacing electronic
switching with optical switching.

Several architectures utilizing optical switching technologies have been
proposed for the aggregation and core tiers.

Figure 12 shows an example of a hybrid solution DC3 , where electronic packet
switching is applied for small-volume data flows (referred to as mice flows)
and optical circuit switching technology is applied for high-volume data flows
(referred to as elephant flows). In addition, the aggregation and core tiers
are merged in the core switches, which can also reduce the energy consumption
of the switching system.

Figure 12: Helios, A Hybrid Electrical/Optical Switch Architecture for DCs DC3

In order to further reduce energy consumption and cabling complexity merging
aggregation and core tiers by all-optical switching architectures has been
proposed, as shown in Figure 13 DC4 . The optical switching can be based on
either optical circuit switching DC5  or optical packet switching DC6 . In
both cases the optical switching architectures require active optical
switching devices, e.g., wavelength selective switches (WSSs) and optical
space switching matrices. Additionally, in optical packet switching the
buffering for contention resolution is performed in electronic domain.

Figure 13: DC network architecture with merged core/aggregation tier based on
optical switching DC4

An example of the circuit switching based optical DC network architecture DC5
referred to as Optical Switching Architecture (OSA) is shown in Figure 14,
while Figure 15 shows an example of an optical packet switched DC network DC6
referred to as low-latency interconnect optical network switch (LIONS), where
packet contention resolution is performed by electronic buffers, and therefore
extra optical/electrical (OE) and electrical/optical (EO) conversions are
needed.

Figure 14: Optical Switching Architecture (OSA), the optical circuit switching
based DC network DC5  Figure 15: Low-Latency Optical Switch for DCs based on
AWGR (LIONS) DC6 . AWGR: arrayed waveguide grating router.

It has been shown that applying optical switching technologies in
core/aggregation tier can achieve almost 50% reduction of the energy
consumption per bit compared to DC networks based solely on EPSs DC8 . The
reason for this limitation is that a lot of energy is consumed by the EPSs in
the edge, i.e., at ToR. Therefore, to further reduce the energy consumption,
passive optical interconnects (POIs) at ToR have been proposed in DC7 . It has
been shown that these architectures are able to reduce energy consumption per
bit by a factor of 10 DC7 . The POI concept has been initially introduced to
replace EPSs at ToR of the large DCs as shown in DC7 . However, the research
efforts published in DC8  addressed the extension supporting medium-size DCs
and/or the aggregation/core tier of the large DCs. The main idea of the POI is
to realize interconnects by using only passive optical components, such as
optical power splitters/couplers and wavelength splitters, i.e., arrayed
waveguide gratings (AWGs).

Figure 16: AWG based POI (left) and coupler based POI (right). WTT: wavelength
tunable transmitter, AWG: arrayed waveguide grating, ONI: optical network
interface, RX: receiver, WTF: wavelength tunable filter, WSS: wavelength
selective switch. DC7

Figure 16 shows two architectures of POI on ToR DC7 , namely AWG based (on the
left) and coupler-based POI (on the right). The first option requires N+K
wavelengths to support up to N servers within a rack, where N wavelength
channels are assigned for communication between any pair of servers within the
rack and K wavelengths are reserved for communication between the servers and
uplink ports. A strict wavelength plan is needed following the cyclic property
of the AWG. It reduces the architecture flexibility. In the second option, an
(N+1)x(N+1) coupler also interconnects N servers in a rack. Each optical
network interface (ONI) on the server is connected to a pair of the input and
output ports of the coupler. The last input/output port of the coupler is
reserved for connection to a wavelength selective switch (WSS) that chooses
the wavelengths assigned to the communications between the servers and uplink
ports and blocks the wavelengths for the communications between any pair of
servers within the rack. The WSS here is an active component, so the overall
interconnect architecture is not fully passive. To keep the architecture
strictly passive, the WSS can be eliminated. However, it would lead to
inefficient wavelength utilization. The broadcast nature of the coupler-based
POI gives this architecture higher flexibility in wavelength allocation
compared to the AWG based POI where a fixed wavelength plan is needed. On the
other hand, an additional component, e.g., a wavelength tunable filter (WTF),
is required in ONI in order to select the specific wavelength assigned to the
certain communication.

The POI architectures illustrated in Figure 16 have been shown to be very
energy efficient. This was confirmed by the results presented in Figure 17 DC8
showing the comparison in terms of energy consumption per bit (on the left)
and power consumption (on the right) between OSA and LION architectures (incl.
EPS based ToR) with the DC network architecture based on flex grid optical
switch in the core tier and POI at ToR, referred to as EODCN. A DC network
based on EPSs is used as a benchmark.

Figure 17: Comparison of energy consumption per bit and power consumption as a
function of number of servers in DC networks based on different switching
technologies. EDCN: DC network based on EPSs EODCN: DC network based on flex
grid in the core tier and POI at ToR . DC8

###  5.2 Future Developments

As discussed previously, to address the drastically increasing intra-DC
traffic in a cost and energy efficient way, further improvement of
scalability, resource utilization and reduction of cabling complexity is
needed. This sub-chapter highlights resource disaggregation and spatial
division multiplexing (SDM) as powerful means for solving resource utilization
and capacity problems in future DC networks.

#### Resource disaggregation

As already mentioned, the major reason that limits resource utilization in DCs
is a possible mismatch in terms of compute resources required by the various
applications and the amount of CPU, memory and storage integrated in the
physical blade servers. This problem can be mitigated by disaggregating
different resources in DCs and utilizing them according to the demand. In
contrast to the integrated blade server, where fixed amount of compute
resources is integrated in the server chassis, the disaggregated DCs contain
decoupled resources held on different resource blades. The resource
disaggregation has a great potential to improve resource utilization, but it
requires ultra-high bandwidth and ultra-low latency links interconnecting
different types of resources, bringing a significant challenge for intra-DC
interconnects.

Recently, partial resource disaggregation was widely deployed in DCs, in which
the storage is decoupled and connected to the other computing resources (such
as CPU and memory) through external switch fabrics. A more aggressive
solution, i.e., fully disaggregated architecture (see Figure 18), in which
every type of resources, including CPU, memory, storage, NIC, and
accelerators, such as graphics processing units (GPUs) (not shown in the
figure), are completely decoupled from each other DC9 . It enables DC
operators to replace/upgrade any type of resources separately and when
necessary, leading to significantly improved resource utilization and
availability performance. In this scenario a hypervisor hyp  can be used to
monitor and manage the virtual machines (VMs) running in different resource
blades. The CPU-related interconnects, i.e., the ones between the CPU and
memory, as well as between the CPUs are most bandwidth-demanding in this kind
of architectures. These connections usually require peak bandwidths higher
than 400 Gb/s and therefore need optical interconnects offering ultra-high
capacity. The main difference between the two approaches shown in Figure 18,
namely the all-optical and hybrid, is a need for the electronic switch in the
hybrid architecture as well as the required additional regular optical
interfaces (OIs). This difference may cause a substantial increase in energy
consumption and extra complexity on cabling. On the other hand, the
communication coordination in the all-optical architecture may be more
complex, because every communication link from/to a resource blade is handled
by the single ultra-high capacity optical interface (OI). This drawback may
lead to a negative impact on the latency performance. The memory blades also
need special considerations, so that the ultra-high bandwidth CPU-memory
communication does not use up the OI bandwidth all the time, starving the
memory-storage and memory-NIC communications. Such trade-offs need to be fully
explored to determine the proper architecture for further investigation.

Moreover, it is important to develop resource allocation algorithms for VM
deployment by considering the low-latency requirements to jointly optimize
energy consumption and usage of computing, storage and communication resources
in the time, spectrum and space domains. Meanwhile, managing the traffic
burstiness in DCs is extremely challenging. Recent advances in machine
learning, such as supervised and reinforcement learning strategies, might be
helpful to might be helpful to construct a traffic arrival prediction system
that is capable of learning from past traffic flows. Accurate prediction of
the DC traffic is critical, not only to efficiently manage, provision, and
reconfigure network resources and services, but also to mitigate the optical
system capacity limitations.

Figure 18: Fully disaggregated architectures with all-optical (left) and
hybrid (right) interconnect approaches . DC9

#### Spatial division multiplexing (SDM)

The current intra-DC networks use either individual fibers or fiber ribbons,
which are costly, bulky, hard to manage and not scalable. SDM fibers, in
particular multi-core fibers (MCFs), are regarded as a feasible and efficient
way to address these problems. Moreover, as previously mentioned, ultra-high
capacity is required to support fully disaggregated DCs, which is an
additional driving force for implementing SDM fibers in DC networks. Figure 19
shows the potential placement of SDM techniques in future DC networks. DC10
provides a detailed overview of the components and transmission options
outlining the SDM development trend. This sub-chapter focuses on the technical
directions related to the co-existence of SDM and wavelength/spectrum division
multiplexing in attempt to address ever-increasing capacity demand and
resource utilization problem in DC networks.

Figure 19: Potential placement of SDM techniques in future DC networks . DC10
Figure 20: Combined SDM and spectrum multiplexing based optical interconnect
options (from top to bottom): A1 uncoupled SDM, A2 uncoupled SDM and flex-
grid, A3 coupled SDM with spectral flexibility, A4 coupled SDM with spectral
and spatial flexibility and A5 coupled SDM with restricted spectral and
spatial flexibility. SSS: spectrum selective switch, SSSS: spectrum and space
selective switch, fi/fo: fan-in/fan-out, Req.: connection request. DC10

Figure 20 shows 5 candidate SDM-based intra-DC network architectural options
(more details about this figure combined SDM and spectrum multiplexing based
optical interconnect options can be found in DC10 ; DCF ):

  * •

Architecture A1 referred to as the uncoupled SDM architecture, applies only
switching between independent spatial elements.

  * •

Architecture A2 referred to as the uncoupled SDM with flex-grid architecture,
allows each spatial element to work as a separate flex-grid transmission
medium. As A2 keeps each spatial element independent, it requires spectrum
selective switch (SSS) for each spatial element and two spatial fan-in/fan-out
(fi/fo).

  * •

Architecture A3 referred to as the coupled SDM with spectral flexibility
architecture, expands spectral superchannels in the spatial domain to create
spectral-spatial superchannels with spatial coupling. Therefore, two large
SSSs need to be employed.

  * •

Architecture A4 referred to as the coupled SDM with spectral and spatial
flexibility architecture, offers an unrestricted flexibility in both spectral
and spatial domains, which leads to the highest flexibility degree compared to
the other architectures. On the other hand, A4 requires a large spectrum and
space selective switch (SSSS) to interconnect the SDM fiber. Moreover, because
of the high flexibility, advanced resource allocation approaches are needed to
ensure efficient resource utilization.

  * •

Architecture A5 referred to as the coupled SDM with restricted spectral and
spatial flexibility architecture, can establish flexible spectral-spatial
superchannels but needs to make sure that the spectral-spatial superchannels
belonging to the same spectral group utilize the same spectrum resources at
all spatial elements. A large SSSS is required to connect the SDM fiber.
Compared to the unrestricted flexibility in A4, the restricted flexibility in
A5 reduces the complexity of the resource allocation and cost of the hardware,
reflected by the lower amount of SSSs.

Performance comparison of the combined SDM and wavelength/spectrum division
multiplexing architectures shows that higher resource allocation flexibility
leads to more efficient resource utilization. However, it should be noted that
the highest flexibility comes with the highest cost, which is not always
necessary, particularly in relatively small DCs. Meanwhile, SDM might
introduce extra latency, not only because of extra complexity caused by the
signal processing process for handling inter-spatial channel impairments, but
also by potential additional level of switching in the space domain (e.g.,
shown in A4 and A5). This latency impact should be further investigated.

##  6 Conclusion

This chapter highlighted the importance of optical networks for the further
development of digitalization, crucial for the societal advance.

First, the chapter provided a brief overview of technology and architectural
evolution of optical access networks, driven initially by increase in
broadband traffic requirements and more recently by requirements for 5G and
beyond services. While the technology has evolved steadily towards higher data
rate per channel and multiple wavelengths, the proposed architecture has
changed in different ways. For example, the concepts of ultra dense WDM, Long-
Reach PON and mesh PON architectures have been described. The evolution
towards virtualized implementation has also been presented. It can bring new,
unprecedented flexibility to access networks, an important feature to support
evolutions towards novel, often unpredictable, services and applications.

In optical core networks, the fast development of optical network devices
makes it possible to both increase transmission capacity and improve optical
network flexibility, resource efficiency and performance. Different planning
problems have been discussed with the emphasis on the optimal resource
allocation and reliability performance. Moreover, different options for
optical network control and management have been briefly described.

Finally, the main challenges for intra-DC interconnects, such as increasing
traffic demand and power consumption, have been highlighted. These challenges
lead to a necessity of applying optical interconnects inside the DCs. Some
ideas for optical intra-DC network architectures have been reviewed and
compared. and resource disaggregation and SDM have been identified as future
developments to address the rapidly growing traffic demand and to improve
power and cost efficiency.

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