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This paper presents an experimental evaluation of network protocols for control and management of optical networks and optical network equipment as seen in current trends. This paper presents the YANG data modeling language and its associated RESTCONF/NETCONF protocols. Later, it details multiple data models used in optical networks, such as IETF TEAS, ONF Transport API, OpenROADM, and OpenConfig. It also presents multiple protocols for telemetry (i.e., YANG PUSH, gRPC, and gNMI). Later, a zero-touch SDN controller architecture for multiple standard-defining organizations (SDOs) is presented, to support the usage of multiple protocols in a zero-touch optical network. Finally, the presented protocol implementations are experimentally evaluated and compared in terms of latency and overhead. The paper explores the use of these protocols (e.g., in research, demonstrations, and open-source optical networking projects) and provides results and recommendations for their integration in equipment and networks.

In the present day, the evaluation of machine learning (ML) as a candidate for substituting analytical quality of transmission (QoT) estimators is done in a compartmentalized way. The assessment is not produced from a global optical network design perspective and with accurate optical design metrics; on the contrary, the evaluation heavily focuses on the physical layer impairment precision capabilities while underemphasizing the effects at the network layer. In this paper, we recommend a suitable methodology for evaluating the QoT substitution based on the foundational idea that different QoT estimators should be examined on a comparative basis by analyzing network-relevant metrics at parity of availability performance. Pragmatically, we recommend comparing performance estimation solutions through the aggregate network throughput, i.e., capacity, at the equity of their overestimation likelihood, which drives system margins. To demonstrate the need for such a network viewpoint and illustrate the potential drawbacks of an inadequate assessment of the QoT substitution, we use the proposed method in several scenarios (altering network topologies, input parameter uncertainty conditions, and availability requirements), showing that we can achieve gains in QoT estimation error or design margins while observing notable losses in terms of network throughput. Considering the results were contrary to what one may expect, we decided to undergo a statistical analysis in order to investigate and grasp the consequences of the model error distribution in relation to the network capacity.

Passive optical networks (PONs) are widely deployed worldwide as access networks, and various standards have been created to serve the demands of different markets. In recent years, software-defined-network-enabled broadband access (SEBA) has been studied for the realization of flexible and cost-effective PONs, while the zero touch provisioning (ZTP) function of optical line terminals (OLTs) and optical network units (ONUs) is being studied for automatic management. However, although ZTP for ITU-T standards has already been implemented, ZTP for IEEE standards has not been achieved, since the workflow is significantly different for each access standard due to differences in the authentication method. The subdivided package specifications and medium access control (MAC) dress authentication are barriers that hinder achieving ZTP for the IEEE standards. This paper proposes a workflow driver based on a model that implements authentication compliant with IEEE PON packages A and B according to events produced by SEBA components and specification information obtained from OLT profiles. The feature of our proposal is to fabricate an IEEE-compliant driver package that automatically switches the authentication method by acquiring the technology profile of the orchestrator, and as soon as IEEE802.1X authentication or MAC address authentication is completed, the subscriber setting is registered to the SDN controller in order to enable data plane service. We demonstrate the proposed workflow driver on a system consisting of SEBA-focused commercially available OLT/ONU hardware and open source software. The results show that the orchestrator can automatically provision ONUs and that managing various access devices on a common access platform is feasible.

Soft-failure localization frameworks typically use if-else rules to localize failures based on the received telemetry data. However, in certain cases, particularly in disaggregated networks, some devices may not implement telemetry, or their telemetry may not be readily available. Alternatively, machine-learning-based (ML-based) frameworks can automatically learn complex relationships between telemetry and the fault location, incorporating information from the telemetry data collected network-wide. This paper evaluates an ML-based soft-failure localization framework in scenarios of partial telemetry. The framework is based on an artificial neural network (ANN) trained by optical signal and noise power models that simulate the network telemetry upon all possible failure scenarios. The ANN can be trained in less than 2 min, allowing it to be retrained according to the available partial telemetry data. The ML-based framework exhibits excellent performance in scenarios of partial telemetry, practically interpolating the missing data. We show that in the rare cases of incorrect failure localization, the actual failure is in the localized device’s vicinity. We also show that ANN training is accelerated by principal component analysis and can be carried out using cloud-based services. Finally, the evaluated ML-based framework is emulated in a software-defined-networking-based setup using the gNMI protocol for streaming telemetry.

Quantum Key Distribution (QKD) has been identified as a secure method for providing symmetric keys between two parties based on the fundamental laws of quantum physics, making impossible for a third party to copy the quantum states exchanged without being detected by the sender (Alice) and receiver (BoB) and without altering the original states. However, when QKD is applied in a deployed optical network, physical layer intrusions may occur in the optical links by injecting harmful signals directly into the optical fibre. This can have a detrimental effect on the key distribution and eventually lead to its disruption. On the other hand, network architectures with software defined networking (SDN) benefit from a homogeneous and unified control plane that can seamlessly control a QKD enabled optical network end-to-end. There is no need for a separate QKD control, a separate control for each segment of an optical network and an orchestrator to coordinate between these parts. Furthermore, SDN allows customised and application tailored control and algorithm provisioning, such as QKD aware optical path computation, to be deployed in the network, independent of the underlying infrastructure. Therefore, in this manuscript, we investigate the integration of the application, SDN and QKD infrastructure layers and confirm capability for flexible supervision and uninterrupted key service provisioning in the event of link level attacks. An experimental demonstrator is used, for the first time, to verify the architecture proposed, considering real-time monitoring of quantum parameters and fiber-optic link intruders to emulate real-world conditions. Furthermore, attacks on a standard single-mode fiber (via a 3dB coupler) and a multicore fiber (via an adjacent core) are undertaken to explore different connectivity between QKD units. Results show an additional attacker identification and switching time of less than 60ms for the link cases investigated, being negligible compared to the total (re)-initialization time of 14 minutes of the QKD units.

In recent years, optical networks have become more complex due to traffic increase and service diversification, and it has become increasingly difficult for network operators to monitor large-scale networks and keep track of communication status at all times, as well as to control and operate the various services running on the networks. This issue is motivating the need for autonomous optical network diagnosis, and expectations are growing for the use of machine learning and deep learning. Another trend is the active movement toward reducing capital expenditure (CAPEX)/operational expenditure (OPEX) of optical transport equipment by employing whitebox hardware, open source software, and open interfaces. In this paper, we describe in detail the concept of a series of workflows for the whitebox transponder, including getting optical performance data from the coherent optical transceiver, diagnosing optical transmission line conditions by applying deep neural networks (DNNs) to the collected data, and notifying the remote network management system (NMS) of the diagnosis results. In addition, as one of the use cases, we demonstrate fiber bending detection based on the diagnosis workflow. Offline and online demonstrations show the deployed diagnosis system can identify the fiber bend with up to 99% accuracy in our evaluation environment.

The network operator’s call for open, disaggregated optical networks to accelerate innovation and reduce cost, make progress in the standardization of interfaces, and raise telemetry capabilities in optical network systems has created an opportunity to adopt a new paradigm for optical network design. This new paradigm is driven by direct measurement and continuous learning from the actual optical hardware deployed in the field. We report an approach towards practical, vendor-agnostic, real-time optical network design and network management using a combination of two learning models. We generalize our physics-based optical model parameter estimation algorithm using the extended Kalman state estimation theory and, for the first time, to the best of our knowledge, present results using real optical network field data. An observed 0.3 dB standard deviation of the difference between typical predicted and measured signal quality appears mostly attributable to transponder performance variance. We further propose using the physics-based optical model parameter values as inputs to a second learning model with a recurrent neural network such as a gated recurrent unit (GRU) to allocate the appropriate required optical margin relative to the typical signal quality predicted by the physics-based optical model. A proof of concept shows that for a dataset of 3000 optical connections with a wide variety of amplified spontaneous emission noise and nonlinear noise limited conditions, a 10-hidden-unit 2-layer GRU was sufficient to realize a margin prediction error standard deviation below 0.2 dB. This approach of measurement data-driven automated network design will simplify deployment and enable efficient operation of open optical networks.

Availability is a key service metric when deploying service function chains (SFCs) over network slices in 5G networks. We study the problem of determining the composition of a slice for a service function chain and the mapping of the slice to the physical transport network in a way that guarantees availability of the SFC while minimizing cost. To improve the availability, we design a slice that provides multiple paths (possibly with non-disjoint routing over the physical infrastructure) for hosting SFCs, and we determine the appropriate dimensioning of bandwidth on each path. Our simulation results show the effectiveness of our approach in terms of the cost of establishing the SFC and the SFC acceptance ratio.

SDN Enabled Broadband Access (SEBA) is a large open-source development and integration project hosted by the Open Networking Foundation (ONF). Built using white-box hardware, merchant silicon, and SDN principles, SEBA brings the benefits of virtualization and cloudification to passive optical network (PON)-based broadband access networks for FTTH/FTTB deployments. Already in field trials with several Tier-1 operators, SEBA is rapidly moving toward production readiness with contributions from a rich open-source community of operators, vendors, and system integrators.

In this paper, we identify challenges in developing future optical network infrastructure for new services based on technologies such as 5G, virtual reality, and artificial intelligence, and we suggest approaches to handling these challenges that include a business model, architecture, and diversity. Through activities in multiservice agreement and de facto standard organizations, we have shown how the hardware abstraction layer interfaces of optical transceivers are implemented for multivendor and heterogeneous environments, coherent digital signal processor interoperability, and optical transport whiteboxes. We have driven the effort to define the transponder abstraction interface with partners. The feasibility of such implementation was verified through demonstrations and trials. In addition, we are constructing an open-transport platform by combining existing open-source software and implementing software components that automate and enhance operations. An open architecture maintains a healthy ecosystem for industry and allows for a flexible, operator-driven network.

We propose a new automation concept for access network equipment called “Plug & Provision.” This concept aims at reducing the workload in provisioning passive optical network (PON) service by utilizing a modular optical line terminal (OLT) and automating the setup sequence. To realize this concept, the automation platform must automatically detect the modular OLT plugged into a switch port and execute the configuration sequence that it triggered. We utilize open-source software in the control/management layer, especially focusing on OpenFlow as a control protocol. We present the overall architecture including a newly proposed PON abstraction and in-band control method for deeply disaggregated modular OLTs, and we demonstrate the feasibility of the architecture.

We propose a new optical access network concept named “Mini-PON.” This concept features the separation of passive optical network (PON)-optical line terminal (OLT) functions into micro module-type hardware that offers switch port pluggability and minimal modular software running on a server to literally minimize the space needed for PON deployment, giving more flexibility to operators who want to deploy PONs in various fields quickly and cost effectively. We explore the optimal function split between hardware and software to balance performance and flexibility. A prototype demonstrates that the proposed implementation architecture can perform as well as a conventional PON in terms of throughput and upstream latency, while also realizing (i) dynamic bandwidth allocation softwarization and modularization via standards-based application programming interfaces and (ii) remote placement of the hardware module from the central office on which the OLT software runs: both are key advances.

Open optical networks present a variety of benefits to service providers and enterprise network operators, such as freedom from single-vendor dependence and the potential to select best-in-class solutions for each individual network role (e.g., line system or optical terminals). In this paper, we discuss two open optical network models: one enables transponder–line system interoperability, whereas the other goes a step further and enables line system–line system interoperability. We examine these models in more detail in the context of an open optical system already deployed at scale. We present real-world experiences and lessons learned with respect to optical link design, software and controls, deployment, and operation, as well as recommendations for future evolution of the network.

Multi-vendor interoperability and disaggregation are attracting the interest of network operators as a way to avoid vendor lock-in, thus opening the market and, possibly, reducing costs. NETCONF, in concert with YANG data models, has been identified as the software defined networking (SDN) configuration protocol for these networks. Currently, several YANG models describe devices in a vendor-neutral way; however, there is a lack of YANG models describing functions. Moreover, the centralization of the control plane may suffer from scalability issues during critical situations (e.g., link failures), given that several restoration requests will arrive at the SDN controller close in time. This paper investigates an innovative paradigm of restoration for disaggregated SDN networks named delegated restoration, which operates in a hybrid centralized–distributed manner. Before a failure occurs, the backup lightpath is centrally computed by the SDN controller, and, based on this computation, the controller informs the network devices (switches and transponders), through NETCONF, of the reconfigurations to perform in case of failure. Simulations show that delegated restoration can reduce the restoration time with respect to a fully centralized approach, making it a candidate for impacting the operation, administration, and maintenance of next-generation networks.

Quantum key distribution (QKD) is a physical technology that enables the secure generation of bit streams (keys) in two separated locations. This technology is designed to provide a solution for very secure (quantum-safe) key agreement, which is nowadays at risk due to advances in quantum computing. The recent demonstration of a QKD network in the metropolitan area of Madrid shows how these networks can be deployed in current production infrastructure by following existing networking paradigms, such as software-defined networking. In particular, a three-node QKD network is implemented on the metropolitan area network using existing infrastructure and coexisting with other data and control services. On the other hand, telecommunication networks are drastically changing the way services are architectured. Users of the operator’s infrastructure are moving from traditional connectivity services (e.g., virtual private networks) to a set of interconnected network functions, either physical or virtual, in the shape of service function chaining (SFC). However, SFC users do not have a method to validate that the traffic flow is appropriately forwarded across the nodes in the network, a situation that may lead to very critical security breaches (e.g., a security node or a firewall in the chain that is bypassed). This work presents a method for validating ordered proof-of-transit (OPoT) on top of the Madrid Quantum Network. We first provide a general description of the QKD network deployed in Madrid. Then, we describe an existing security protocol for PoT in packet networks, analyzing its issues and vulnerabilities. Finally, this work presents a protocol for alleviating the security breach found in this work and for providing OPoT in SFC. Finally, an example of the real implementation is shown, where nodes being part of the OPoT scheme are provisioned with QKD-derived keys.

Focused on reducing capital expenditures by opening the data plane to multiple vendors without impacting performance, node disaggregation is attracting the interest of network operators. Although the software-defined networking (SDN) paradigm is key for the control of such networks, the increased complexity of multilayer networks strictly requires monitoring/telemetry and data analytics capabilities to assist in creating and operating self-managed (autonomic) networks. Such autonomicity greatly reduces operational expenditures, while improving network performance. In this context, a monitoring and data analytics (MDA) architecture consisting of centralized data storage with data analytics capabilities, together with a generic node agent for monitoring/telemetry supporting disaggregation, is presented. A YANG data model that allows one to clearly separate responsibilities for monitoring configuration from node configuration is also proposed. The MDA architecture and YANG data models are experimentally demonstrated through three different use cases: i) virtual link creation supported by an optical connection, where monitoring is automatically activated; ii) multilayer self-configuration after bit error rate (BER) degradation detection, where a modulation format adaptation is recommended for the SDN controller to minimize errors (this entails reducing the capacity of both the virtual link and supported multiprotocol label switching-transport profile (MPLS-TP) paths); and iii) optical layer selfhealing, including failure localization at the optical layer to find the cause of BER degradation. A combination of active and passive monitoring procedures allows one to localize the cause of the failure, leading to lightpath rerouting recommendations toward the SDN controller avoiding the failing element(s).

In the next-generation radio access network (NG RAN), the next-generation evolved NodeBs (gNBs) will be, likely, split into virtualized central units (CUs) and distributed units (DUs) interconnected by a fronthaul network. Because of fronthaul latency and capacity requirements, optical metro-ring networks are among the main candidates for supporting converged 5G and non-5G services. In this scenario, a degradation in the quality of transmission of the lightpaths connecting DU and CU can be revealed (or anticipated) based on monitoring techniques. Thus, the lightpath transmission parameters can be adapted to maintain the required bit error rate (BER). However, in specific cases, the original requested capacity between DU and CU could be not guaranteed, thus impacting the service. In this case, another DU–CU connectivity should be considered, relying on a change of the so-called functional split. This study proposes a two-step recovery scheme orchestrating lightpath transmission adaptation and functional split reconfiguration to guarantee the requested connectivity in a virtualized RAN fronthaul. Results show that, for the connections that cannot be transported by the original lightpath, a graceful degradation followed by a recovery is possible within tens of seconds. This work was partly funded by the project H2020-ICT-2016-2 “5G-TRANSFORMER” (761536)

The continuous evolution of elastic optical networks (EONs) toward more efficient and flexible backbone networks has been enabled by the innovation of data and control plane technologies. Network Configuration Protocol (NETCONF) is emerging as a software-defined networking (SDN) protocol for the control of optical networks. Its relevance is due to the fact that, besides providing control functionalities (e.g., data plane device configuration), NETCONF also provides management functionalities (e.g., access to monitoring information). NETCONF may exploit the Yet Another Next Generation (YANG) data model to describe network elements to be controlled/managed. YANG and NETCONF are of interest for operators since they provide a standard way to control and manage network elements independently from the vendor. This paper presents and demonstrates a control and management plane for EONs, based on the NETCONF protocol and YANG. In particular, a YANG model describing optical transponders supporting sliceability, variable rate, and monitoring functionalities is proposed. NETCONF experimental demonstrations are carried out to validate the proposed model and to prove the control and management capabilities of these technologies when applied to EONs.

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. MPLS-over-optical virtual network topologies (VNTs) can be adapted to near-future traffic matrices based on predictive models that are estimated by applying data analytics on monitored origin-destination (OD) traffic. However, the deployment of independent SDN controllers for core and metro segments can bring large inefficiencies to this core network reconfiguration based on traffic prediction when traffic flows from metro areas are rerouted to different ingress nodes in the core. In such cases, OD traffic patterns in the core might severely change, thus affecting the quality of the predictive OD models. New traffic model re-estimation usually takes a long time, during which no predictive capabilities are available for the network operator. To alleviate this problem, we propose to extend data analytics to metro networks to obtain predictive models for the metro flows; by knowing how these flows are aggregated into OD pairs in the core, we can also aggregate their predictive models, thus accurately predicting OD traffic and therefore enabling core VNT reconfiguration. To obtain quality metro-flow models, we propose an estimation algorithmthat processes monitored data and returns a predictive model. In addition, a flow controller is proposed for the control architecture to allow metro and core controllers to exchange metro-flow model information. The proposed model aggregation is evaluated through exhaustive simulation, and eventually experimentally assessed together with the flow controller in a testbed connecting premises in CNIT (Pisa, Italy) and UPC (Barcelona, Spain).

In optical transport networks, the urgent demand for control efficiency and intelligence has become one of the most significant challenges for telecom operators. With the development in control technology, more attention has been paid to performance enhancement of the centralized controller of software-defined optical networks (SDONs). Meanwhile, machine learning (ML) is emerging as a promising technology to facilitate the intelligence of control planes in SDONs. Some research works have been conducted to use ML to solve problems in optical transport networks. However, it is still a challenge to deploy and use computing resources. On the one hand, computing resources can be deployed inside the centralized controller of an SDON to enable control layer artificial intelligence (AI). On the other hand, computing resources can also be deployed on the hardware board to enable on-board AI. The two-layer AI functions are able to meet different intelligent requirements in data and control layers in different scenarios. Therefore, coordination between them is an important issue. In this paper, a novel control architecture based on an SDON is proposed, and it can support control layer AI and on-board AI simultaneously. Particularly, on-board AI is proposed based on edge computing to support various ML applications. To evaluate the proposed architecture, we develop an experimental testbed and demonstrate a typical use case, i.e., alarm information prediction. Experimental results show that coordination and cross-layer optimization between control layer AI and on-board AI can be achieved. However, there is much space for research in this area, and we envision some open issues.

Network operators will require autonomous operation with the help of artificial intelligence to reduce operational expenditures and to create networks that are easier to manage and maintain. However, not all operators operate in the same way. Therefore, we may have different operator-specific use cases for such autonomous operations. Differences may arise from the range and variety of services and social and/or cultural difference/background. However, we believe that there should be a common framework to achieve such an autonomous world in network operation. We discuss such operator-specific use cases and requirements in this paper. Then, we propose a possible common network diagnosis framework (the CAT cycle) and its platform to meet the requirements.

Maximizing the delivered capacity of optical transponders by mining the SNR margin to a near-zero level is of critical importance for the economic viability of future optical networks. We examine this trend from the equipment design point of view and apply artificial intelligence to the problem of capacity maximization. The bottom-up approach is focused on providing the key metrics that are built on the fundamentals of network performance, whereas the top-down approach uses machine learning and policy-driven actions that have the promise to achieve an unprecedented level of control. These methods show promise in delivering a practical near-zero margin network deployment.

Centralized radio access network (C-RAN) has been considered as an architectural solution able to reduce capital and operational expenditure in dense 5G cellular networks while allowing better network performance. The C-RAN approach decouples baseband units from antenna sites and places them in selected locations, connected by the so-called fronthaul links. These links require expensive high-capacity connections, thus calling for cost-efficient deployment. This paper presents a hybrid fronthaul solution for C-RAN based on both optical fibers and free-space optics (FSO) to enhance fronthaul flexibility and minimize deployment costs. Two design strategies based on integer linear programming are proposed for both greenfield and brownfield deployments. The first strategy is referred to as joint planning (JP) and is based on the joint minimization of the number of deployed remote radio heads (RRHs) and the cost of the hybrid fiber/FSO fronthaul. The second strategy is based on two-step disjoint planning (DP) that first identifies a cost-optimal RRH placement and then finds the corresponding minimum cost deployment for the fronthaul links. Results obtained with JP and DP are compared in dense urban area scenarios (i.e., with characteristics similar to festivals or concerts), highlighting the advantage of the JP approach compared to DP, both in terms of costs and an enhanced flexibility during the network design process.

Fifth-generation (5G) systems will introduce several new requirements impacting the mobile transport network. Understanding the impact of 5G systems on trans­port networks, both wireless and fixed, is essential to the successful introduction of 5G. This paper provides an over­view of the ongoing work supporting the specification of open interfaces between the different 5G building blocks. It also presents several technologies that can facilitate ef­fective transport networks, describes the architectural challenges in designing flexible 5G transport networks, and provides dimensioning guidelines.

A fronthaul design for current and future mobile networks based on the transport of sampled radio signals from/to base station baseband processing units (BBUs) to/from remote radio heads (RRHs), is presented. The design is a pure-Ethernet switched architecture that uses virtual local area network (VLAN) identifiers for the RRHs and flow identifiers for the antenna ports, and is compatible with current standardization definitions. A comprehensive analysis for the limits of the Ethernet fronthaul in terms of the total number of antennas that can be supported is carried out, based on the latency imposed by the Ethernet network. The analysis assumes the transportation of control and management (C&M) and timing information (based on the precision-time protocol, PTP) but is valid for other types of background traffic (for example, that generated by the implementation of different longterm evolution (LTE) functional subdivisions, in a fronthaul with mixed processing). A low-cost testbed using “smart SFP” in-line probes is presented and used to obtain measurements from an Ethernet fronthaul, transporting mixed traffic. The measurements show how background traffic affects hybrid-automatic repeat request (HARQ) retransmissions, and are used to validate the analysis. The effects of contention of PTP packets is discussed and a simple solution to overcome the effects of contention is proposed.

To accommodate the service paradigm shift driven by the now amazingly wide variety of services, telecom operators must overcome many challenges in redesigning their network infrastructure to attain system flexibility, scalability, and cost effectiveness. A key part of the solution is the recent advent of network virtualization based on commodity hardware. We propose a unique edge-server architecture that combines network virtualiza- tion with advanced digital signal processing (DSP) technology. Our architecture has the server's central processing unit running application layer functions and a generalpurpose accelerator running dissimilar physical layer functions, including advanced DSP. To elucidate the power budget capability of our coherent detection scheme, a hardware experiment demonstrates the optical power budget of 45.2 dB for quadrature phase-shift keying signals of 5 Gbit/s bit rate.

Next-generation edge nodes interfacing innovative IT clusters, 5G fronthaul, and internet of things (IoT) gateways to the optical metro/core network will require advanced and dynamic online quality of service (QoS) per-flow traffic treatment, assuring ultra-low latency requirements. However, current software-defined networking (SDN) implementations (e.g., OpenFlow) do not support forwarding procedures based on the network state, profile variations, and the history of flow statistics at the node level. Currently, such procedures require intervention by the SDN controller, leading to scalability issues and additional latency in data plane forwarding. Moreover, severe security challenges are expected to affect such nodes and threaten IT resources. Thus, increasing bandwidths will require direct deep packet inspection to avoid involvement of the SDN controller, as performed currently, or dedicated and costly security systems. This paper leverages on the potential of the programming protocol-independent packet processors (P4) open source language, recently introduced by the inventors of OpenFlow, to program the data plane structure and behavior of an SDN switch. P4 is able to instantiate custom pipelines and stateful objects, enabling complex workflows, user-defined protocols/headers, and finite state machines enforcement. Moreover, P4 allows portable implementations over different hardware targets, thus opening the way to open source fully programmable devices. Special effort is dedicated to motivate and apply P4 within a multilayer edge scenario, proposing the architecture and the applicability of an SDN P4-enabled packet-over-optical node. Moreover, three specific multilayer use cases covering dynamic traffic engineering (TE) (e.g., traffic offload and optical bypass) and cybersecurity (e.g., distributed denial of service port scan) are discussed and addressed through P4-based solutions. Experimental evaluations have been conducted over a multilayer SDN network exploiting reference P4 software switches (i.e., the behavioral model version 2, or BMV2) and field-programmable gate arrays (FPGAs) at 10 gigabit Ethernet optical interfaces. Extensive results report effective dynamic TE and cybersecurity mitigation enforcement at P4 switches without any controller intervention, showing excellent scalability performance and overall latencies practically in line with current commercial OpenFlow switches.

To quickly meet the diverse requirements imposed by emerging new services on optical access networks, we recently proposed the new concept of "flexible access system architecture" (FASA), which features the disaggregation of time-critical applications. This paper introduces our activities on FASA along with related technical trends. As our key ongoing challenge, the modularization of the dynamic bandwidth assignment function in a passive optical network is reviewed, and information on its use cases, approach, and sequences is given. Furthermore, a reference hardware implementation of FASA for mobile service is described.

Predeployment of regenerators in a selected subset of network nodes allows service providers to achieve rapid provisioning of traffic demands, high utilization, and reduced network operational costs, while still guaranteeing lightpath quality of transmission. Enabled by bandwidth-variable transceivers in flexible-grid optical networks, optical channel bandwidths are no longer fixed but constantly changing according to real-time communication requirements. Consequently, the data-rate-variable traffic together with other new network features introduced by flexible-grid networks will render the regenerator allocation very difficult due to the complicated network states. In this paper, we investigate how to allocate regenerators robustly in flexible-grid optical networks to combat physical-layer impairments when the data rates of traffic demands are random variables. The Gaussian noise model and a modified statistical network assessment process framework are used to characterize the probabilistic distributions of physical-layer impairments for each demand, based on which a heuristic algorithm is proposed to select a set of regenerator sites with minimum blocking probabilities. Our method achieves the same blocking probabilities with on average 10% less regenerator sites compared with the greedy constrained-routing regenerator allocation method, and obtains blocking probabilities two orders of magnitude lower than that of the routing and reach method with the same number of regenerator sites.

To cope with the energy inefficiency as well as the temporal uncertainty of real-world traffic in all-optical backbone networks, we explore the performance gains obtained from adaptively putting network elements into sleep mode, taking into account physical layer impairments (PLIs). Despite recent progress on link sleep mechanisms, the beneficial impacts of periodically activating and deactivating line amplifiers are seriously restricted by extra incurred operational expenditures due to accelerated aging of network equipment, which is a direct consequence of temperature fluctuations. In this paper, we revisit the problem of green PLI-constrained lightpath establishment, paying close attention to minimizing the number of on/off transitions. Toward this end, we formulate green lightpath establishment as a nonlinear multi-objective optimization problem, which addresses not only the energy efficiency, but also the grade of service and quality of service, using accurate models of a wide variety of linear/nonlinear PLIs. To tackle the developed problem under realistic scenarios, we propose the so-called green adaptive time-aware algorithm, which consists of lightpath establishment as well as wake-up/sleep procedures. The presented analysis followed by verifying simulations confirms that the proposed algorithm stands as a practical solution to the cost-efficient green impairment-constrained lightpath establishment problem under temporal uncertainly of incoming traffic.

Elastic optical networks (EONs) offer several optimization dimensions that can be harvested to increase network efficiency and survivability. More specifically, the adaptation of the modulation format to a more robust one can recover the quality of transmission (QoT) of a lightpath affected by a soft failure (i.e., a physical layer degradation resulting in QoT deterioration). In this scenario, increasing the robustness of a transmission may require a spectrum increase. Thus, the spectrum also might have to be reconfigured. In this paper, we propose spectrum sharing between lightpaths of classes requiring different rate guarantees. In the case of a soft failure, a high-priority, rate-guaranteed lightpath can expand its spectrum to recover its QoT at the expense of spectrum allocated to a lower-priority, guaranteed minimum rate lightpath. We propose an integer linear programming (ILP) routing and spectrum assignment (RSA) algorithm for the planning phase and an online RSA for the operation phase of the network that optimize spectrum sharing among lightpaths of different classes. Using these proposed techniques can effectively reduce the network costs to guarantee the rate of high-priority connections.

Breakthroughs in silicon photonics are changing the economics of network hardware and are enabling new directions in computer networking, compelling us to reexamine our previous assumptions regarding computer and network architectures. One such area that bears further study is the choice of network topology in a world where the distance between nodes is largely irrelevant, and where there is no inherent economic advantage to spatial locality. Network architects should be able to select topologies based on their merits and not merely from tradition. Combined with relaxed system-level packaging constraints, silicon photonics will likely lead to future datacenter architectures that look very different from today’s solutions. We present the design and implementation of Topanga, a packet switch optoASIC with the requisite technologies to free datacenter topologies from constraints induced by limited electrical reach. By means of our platform for integrating silicon photonics inside the package, Topanga provides optical I/O with lower energy per bit at lower cost per port than existing solutions.

We consider a unidirectional design approach to tackle the traffic asymmetry in an elastic optical network (EON). We decouple a bidirectional transponder into a pair of separate unidirectional transmitters and unidirectional receivers, and consider different multi-flow transmitters that use either an array of laser diodes or a broadband laser source with filters to generate multiple sub-carriers. To evaluate the benefit of this unidirectional design, we have considered the routing and spectrum allocation optimization problem for the EON. An integer linear programming model and an efficient heuristic algorithm based on spectrum window planes have been developed. Simulation results show that the proposed unidirectional design approach can significantly improve capacity utilization and minimize network cost compared to one based on bidirectional symmetric traffic flows. It is also efficient for network scenarios with spatial traffic asymmetry and where multi-period network planning is required.

This paper reports on the results of the design, fabrication, and experimental demonstration of a wavelength routing Thin-CLOS system based on arrayed waveguide grating routers (AWGRs) for intra-data-center applications. By using M2 W-port AWGRs, this architecture allows increasing by a factor of M the scalability provided by a wavelength router using a single W-port AWGR. The fabricated Thin-CLOS system has 64 ports and makes use of M2 = 4 AWGRs with W = 32 ports and 100 GHz channel spacing. The system fits in a 1U rack enclosure and consumes less than 10 W. We demonstrated error-free performance at 10 Gb∕s per wavelength with a power penalty of

The exponential growth in emerging applications in datacenters leads to significant increases in traffic and bandwidth consumption in datacenter networks. Finegrained optical switching technology can provide lowlatency end-to-end connection with flexible granularity in intra-datacenter networks. In such a scheme, the centralized network controller will compute an end-to-end path and provide switch state commands for each connection request. However, as network scale and traffic requirements continually increase, the centralized controller will face a significant challenge in low-latency end-to-end connection provisioning under a heavy traffic load. Limited by the controller’s computation and processing performance, the continuously arriving connection requests may block the server's message queue and slow down the controller’s processing rate. In this paper, we propose a multi-controller control scheme for fine-grained all-optical datacenter networks. We take optical time slice switching as an example of fine-grained all-optical switching to illustrate our detailed control scheme. Partitioning rules for the network and controllers’ responsibilities, the routing algorithm, and the signaling process are also discussed. The proposed multi-controller control scheme can greatly reduce each controller’s computation and processing burden and improve network performance. Dynamic datacenter network emulations are launched to verify our proposed scheme. The emulation results indicate that the multi-controller scheme can reduce connection setup delays and request and data failure rates, while the single controller fails to handle all the requests.

The nature of network services has drastically changed in recent years. New demands require new capabilities, forcing the infrastructure to dynamically adapt to new scenarios. Novel network paradigms, such as softwaredefined networking (SDN) and network functions virtualization, have appeared to provide flexibility for network management and services. The reliance on software and commoditized hardware of these new paradigms introduces new security threats and, consequently, one of the most desired capabilities is a strengthened security layer when connecting remote premises. On the other hand, traditional cryptographic protocols are based on computational complexity assumptions. They rely on certain mathematical problems (e.g., integer factorization, discrete logarithms, or elliptic curves) that cannot be efficiently solved using conventional computing. This general assumption is being revisited because of quantum computing. The creation of a quantum computer would put these protocols at risk and force a general overhaul of network security. Quantum key distribution (QKD) is a novel technique for providing synchronized sources of symmetric keys between two separated domains. Its security is based on the fundamental laws of quantum physics, which makes it impossible to copy the quantum states exchanged between both endpoints. Therefore, if implemented properly, QKD generates highly secure keys, immune to any algorithmic cryptanalysis. This work proposes a node design to provide QKD-enhanced security in end-to-end services and analyze the control plane requirements for service provisioning in transport networks. We define and demonstrate the necessary workflows and protocol extensions in different SDN scenarios, integrating the proposed solution into a virtual router providing QKD-enhanced IPsec sessions.

Network virtualization is a key technology for accommodating varying traffic demands on optical networks. Network operators construct a virtual network (VN) by slicing physical resources, and resolve traffic congestion by reconfiguring the VN in response to traffic changes. A typical approach is to configure an optimal VN using the traffic demand matrix. However, since it is difficult for this approach to reconfigure the VN following traffic changes, other methods must be explored. Previously we developed a method that observes only the service quality on the VN, i.e., link-level load information on the VN, and searches for a suitable VN by repeatedly making random changes to the current one. However, in practice this noise-induced method repeatedly reconfigures the VN, which leads to over-reconfiguration. In this paper, we propose a VN reconfiguration framework based on the Bayesian Attractor Model, which models the human behavior of making appropriate decisions by recognizing the surrounding situation. Our framework memorizes a set of VN candidates, each of which works well for a specific traffic situation, then retrieves a suitable VN candidate for the current traffic situation from this set. We use certain patterns of incoming and outgoing traffic at edge routers to characterize the traffic situation, as this information can be obtained more easily than the traffic demand matrix. By identifying the stored traffic situation that is closest to the current one and retrieving a suitable VN, our framework can reduce the over-reconfiguration. Evaluation of our method shows that it can identify the traffic situation by observing the amounts of incoming and outgoing traffic at edge routers; as a result it can decrease the number of VN reconfigurations needed to reach a VN suitable for the current traffic situation when compared to the noise-induced method.

Internet traffic is growing rapidly, driven by new applications such as the Internet of things (IoT). To overcome router scaling limitations, router bypass has been introduced. Traditional bypass is based on provisioning fixed-size bypass channels, which was shown to underutilize link capacity. In software defined networking (SDN), router bypass can be requested as a service by the application layer. This study explores the opportunity to bypass routers efficiently without the link underutilization of the traditional bypass.We combine SDN and dynamic layer provisioning to implement dynamic router bypassing and show how this leads to improved efficiency. A simulation built to compare the traditional bypass with the SDN-based bypass showed that SDN-based bypassing enhanced efficiency by 33%. When bypass traffic is increased, we show that the output capacity of a node increases up to 56%, limited by optical capacity rather than router capacity.

As optical networks become increasingly flexible and software driven, network operators need to reconsider their present mode of network planning and operation, which traditionally relies on long planning periods, performed independently for the IP edges (logical topology) and the optical transport layer (physical topology). Network planning assuming fully loaded end-of-life conditions fails to follow traffic evolution and results in capacity overprovisioning, underutilized equipment, and stranded investments. We argue that it would be beneficial to have shorter upgrade cycles and a multiperiod network planning approach that accounts jointly for the upgrade of the optical but also the IP edges of the network. We formulate the incremental multilayer planning problem of an IP over elastic optical network and propose an integer linear programming (ILP) algorithm to solve it. The ILP model leverages the reconfigurability of both network layers to delay equipment deployment and benefit from cost erosion. Our objective is, through repurposing of existing network resources, to deploy in a period the minimum additional network equipment (capital expenditures) to cope with traffic changes from the previous period but also to minimize changes in transitioning between the two periods (operational expenditures). The proposed planning approaches are validated through simulations based on realistic network scenarios, where we also study the effect of the upgrade period duration.

Network operators have deployed optical transmission systems in their production networks for several years. The transport technology for fiber-optic transmission systems in backbone networks has been a synchronous digital hierarchy. However, network operators started migrating such networks to deploy WDM systems to improve the utilization of the fibers. Network operators can migrate to 100 Gbps solutions that can cope with medium-term bandwidth needs. However, when evolving toward 400 Gbps or 1 Tbps, utilization of the optical spectrum is less efficient if the fixed-grid schemas are maintained. Elastic optical networks (EONs) are based on a flexible allocation of the spectrum and configurable transponders. There is an unprecedented flexibility at the optical layer, thanks to advances in the components to support EONs. To take advantage of such flexibility and unlock the potential of EONs, the control architecture plays a key role. This paper presents the architectural choices, including generalized multiprotocol label switching, path computation element, and software-defined networking using a transport application programming interface. Moreover, this paper presents how the open source Netphony suite enables the validation and testing of all previous control plane architectures for EONs.

In this paper, we discuss energy-efficiency improvements in core networks obtained as a result of work carried out by the GreenTouch consortium over a five-year period. A number of techniques that yield substantial energy savings in core networks were introduced, including (i) the use of improved network components with lower power consumption, (ii) putting idle components into sleep mode, (iii) optically bypassing intermediate routers, (iv) the use of mixed line rates, (v) placing resources for protection into a low power state when idle, (vi) optimization of the network physical topology, and (vii) the optimization of distributed clouds for content distribution and network equipment virtualization. These techniques are recommended as the main energy-efficiency improvement measures for 2020 core networks. A mixed integer linear programming optimization model combining all the aforementioned techniques was built to minimize energy consumption in the core network. We consider group 1 nations' traffic and place this traffic on a US continental network represented by the AT&T network topology. The projections of the 2020 equipment power consumption are based on two scenarios: a business as usual (BAU) scenario and a GreenTouch (GT) (i.e., BAU + GT) scenario. The results show that the 2020 BAU scenario improves the network energy efficiency by a factor of 4.23 x compared with the 2010 network as a result of the reduction in the network equipment power consumption. Considering the 2020 BAU + GT network, the network equipment improvements alone reduce network power by a factor of 20 x compared with the 2010 network. Including of all the BAU + GT energy-efficiency techniques yields a total energy efficiency improvement of 315×. We have also implemented an experimental demonstration that illustrates the feasibility of energy-efficient content distribution in IP/WDM networks.

As enterprises struggle to meet the demand for elastic cloud computing services in distributed data centers (DCs), a significant portion of the total expenditure is spent on the procurement and maintenance of server and network equipment. Network and server resource utilization efficiency is therefore crucial to minimize overall costs and increase return on investment. We present an adaptive and synergistic management of both network and IT resources with an autonomous control plane on a converged inter/ intra-DC network to address this issue. The control plane uses its awareness of both network and IT resource usage to provide dynamic resource allocations to meet quality of service guarantees, while also allowing for reduction in operational expenses by consolidating virtual machines to maintain high CPU usage on the active servers. We built a three-node inter-DC experimental testbed to demonstrate the feasibility of our concept and show that our algorithm successfully provisions both network resources and consolidates IT resources under various load conditions.

Due to the flexibility and adaptivity of elastic optical networks (EONs), IP-over-EON would be a promising infrastructure for next-generation backbone networks. As a backbone network usually carries massive traffic, one always needs to properly address its network survivability issue. The network survivability of an IP-over-EON can be affected not only by the fiber cuts in the EON layer but also by the router outages in the IP layer. In this work, we study how to realize the cross-layer orchestration to address IP router outages with cost-efficient multilayer restoration (MLR) in IP-over-EONs. Specifically, we consider the situation in which a single router outage happens in an IP-over-EON and propose MLR algorithms to minimize the additional operational expense (OPEX) due to MLR. We first design three MLR strategies to fully explore the flexibility and adaptivity of IP-over-EONs. Then, with the strategies, we formulate an integer linear programming (ILP) model to find the MLR scheme in which the additional OPEX due to incremental usages of sliceable bandwidth-variable transponders (SBV-Ts) and frequency slots (FSs) and lightpath reconfigurations is minimized. We also propose an auxiliary graph (AG) based heuristic algorithm to reduce the time complexity. The proposed algorithms are evaluated with extensive simulations, and the results indicate that, compared with an existing benchmark, they can effectively reduce the additional OPEX of MLR.

The data-center interconnection (DCI) market has been rising sharply for some time, leading to the development of networking solutions tailored specifically for it. On the side of the transport network, DCI devices are characterized by simple, footprint-efficient designs focused on scalability, but still leverage the benefits of flexible-rate transponders on the line-side, to extract the most from costly optical transport infrastructures. Accompanying this evolution arose the need for a technology that could provide a flexible client layer where aggregation was done outside the transport device, but flows could still be efficiently mapped to flexible-rate transponder modules. In this context, the appearance of Flex Ethernet (FlexE) is targeted at decoupling the rates of interfaces connecting routers to transport boxes, from the actual flows traversing them. Through the introduction of an additional mapping/de-mapping layer, the capacity of multiple Ethernet interfaces can be virtualized in a resource pool for different flows. This paper addresses the application of FlexE to dense wavelength division multiplexing (DWDM) transport scenarios. Specifically, it evaluates how agnostic to FlexE a transport box should be, considering the potential trade-off between its complexity and the ability to efficiently fill lightpaths. Through a network simulation handling the provisioning of Ethernet flows in a DCI scenario, we evaluate the various FlexE use cases envisioned for transport networks, and how they affect the efficiency of router port usage and transport box provisioning, as well as their effect on the capacity of the DWDM layer.

We have proposed a dynamically configurable and fast optical impairment model for the abstraction of the optical physical layer, enabling new capabilities such as indirect estimation of physical operating parameters in multivendor networks based on pre-FEC BER information and machine learning. BER is commonly reported by deployed coherent transponders; therefore, this scheme does not increase hardware cost. The estimated parameters can subsequently be used to predict optical signal quality at the receiver of not-already-established optical connections more accurately than possible based on the limited amount of information available at the time of offline system design. The higher accuracy and certainty reduce the required amount of required system margin that must be allocated to guarantee reliable optical connectivity. The remaining margin can then be applied toward increased transmission capacity, or a reduced number of regenerators in the network. We demonstrate the quality of transmission prediction experimentally in an optical mesh network with 0.6 dB Q-factor accuracy, and quantify the benefit in terms of network capacity gain in metro networks by impairment-aware network simulation.

Based on fundamental software-defined networking principles, disaggregated solutions have recently been proposed for optical transport networks. The first solution of such type takes the form of disaggregated line systems, which allow network operators to build networks using add/drop multiplexers from multiple vendors. In addition, these line systems allow any specification-compliant optical transponder to be freely connected, independent of their manufacturer. By removing the single-vendor dependency from line systems, traditional business models, based on vertically integrated solutions, are challenged and need to be rethought. Furthermore, competition between vendors is expected to increase, leading to higher innovation and differentiation. However, the development of interoperable line systems sharing a common set of specifications and capabilities may only be able to deliver suboptimal optical transmission performances when compared with proprietary solutions where line systems are fine-tuned. Hence, this paper evaluates the routing performance between disaggregated and proprietary optical line systems. Network simulations are carried out considering two different topologies with single- and multidomain proprietary deployments. The results obtained show that while proprietary line systems keep the capability of achieving the best optical performance, disaggregated solutions cannot be ignored since they have the potential to reduce the optical-electrical-optical interface count with respect to multivendor deployments while still guaranteeing minimal demand blocking.

The transport network paradigm is moving toward next-generation networks that aim at IP convergence, while architectures and technologies are diversifying. Video technologies including ultrahigh-definition TV (more than 33 M pixels) continue to advance, and future communication networks will become video-centric. The inefficiencies of current IP technologies, in particular, the energy consumption and throughput limitations of IP routers, will become pressing problems. Harnessing the full power of light will resolve these problems and spur the creation of future video-centric networks. Extension of optical layer technologies and coordination with new transport protocols will be critical; hierarchical optical path technologies and optical circuit/path switching will play key roles. Recent technical advances in these fields are presented.

Optical wavelength-routed networks enable parallel transmission of massive datasets on nonover-lapping wavelength channels. However, as the sizes of scientific workflows increase, the availability of multi-wavelength resources will fall short of supporting application needs. Rather, these resources must be allocated intelligently, efficiently, and flexibly to bear the burden of high-volume science. We propose lightpath-switching to support modification to the set of wavelength/route resources used to carry and transmit a lightpath signal intermittently throughout its lifetime. Lightpath-switching exposes the scheduler to flexible consumption of unused, fragmented resources during the request schedule but requires neither underlying network technology nor equipment enhancement. We explore the efficacy of lightpath-switching in terms of wavelength-switching, path-switching, and a combination of the two techniques. We prove these problems are NP-complete and develop optimization models and efficient heuristics to quantitatively and qualitatively evaluate the solution space against optimality benchmarks. Our evaluations consider cross-dimension resource consumption from the time, space, and spectrum domains, and our findings indicate great potential for increasing network-wide resource savings, particularly via wavelength-switching. Furthermore, evidence is presented to defend the claim that spectral flexibility has a greater impact on resource utilization efficiency than spatial flexibility.

Optical fiber is the required technology for radio access network (RAN) backhaul and fronthaul. We report on the evolution of RAN equipment, including the advent of virtualization and an investigation of the required architecture and optical access technologies.

Today's networks are quickly evolving toward more dynamic and flexible infrastructures and architectures. This software-based evolution has seen its peak with the development of the software-defined networking (SDN) and network functions virtualization (NFV) paradigms. These new concepts allow operators to automate the setup of services, thus reducing costs in deploying and operating the required infrastructure. On the other hand, these novel paradigms expose new vulnerabilities, as critical information travels through the infrastructure fromcentral offices, down to remote data centers and network devices. Quantum key distribution (QKD) is a state-of-the-art technology that can be seen as a source of symmetric keys in two separated domains. It is immune to any algorithmic cryptanalysis and is thus suitable for long-term security. This technology is based on the laws of physics, which forbids us from copying the quantum states exchanged between two endpoints from which a secret key can be extracted. Thus, even though it has some limitations, a correct implementation can deliver keys of the highest security. In this paper, we propose the integration of QKDsystems with well-known protocols and methodologies to secure the network’s control plane in an SDN and NFV environment. Furthermore, we experimentally demonstrate a workflow where QKD keys are used together with classically generated keys to encrypt communications between cloud and SDN platforms for setting up a service via secure shell, while showcasing the applicability to other cryptographic protocols.