Probabilistic constellation shaping (PCS) has become an attractive technology for future optical networks, able to close the gap to channel capacity at any distance by leveraging its superior fine-grained rate adaptability. In order to get as close as possible to the Shannon limit, PCS selects for any signal-to-noise ratio (SNR) the best performing (square) quadrature amplitude modulation (QAM) constellation and forward error correction (FEC) coding rate. However, higher-order QAM constellations are expected to consume more energy per transmitted bit than lower-order ones. So, we investigate in this paper whether carefully dimensioned SNR margins can lower the usage of higher order QAM constellations for end-to-end optical communications in Flex-Grid over multicore fiber (MCF) optical backbone networks, while still keeping high PCS performance levels. Numerical results obtained in two reference backbone networks disclose very significant reductions on the usage of those higher order QAM constellations when adopting the proposed SNR margins for PCS, at expenses of only minor penalties on the spectral efficiency attainable along the precomputed end-to-end paths.

The use cases associated to the emerging 6G technology demand a dynamic and adaptive network operation capable of addressing the specific requirements of heterogeneous data plane technologies. This demonstration presents the operation of a multi-domain multi-technology control plane architecture designed to provision end-to-end (E2E) connectivity with deterministic performance guarantees. Key innovations highlighted in the demonstration include homogenized configuration of diverse data plane technologies, advanced topology abstraction enabling Key Performance Indicator (KPI)-aware path computation, and the use of digital twin (DT)-based KPI estimation to support deterministic connectivity configuration. These capabilities collectively showcase a promising approach for managing the complexity and performance demands of future 6G networks.

Disaggregated datacenters are promising solutions for executing ML applications. One crucial aspect is the application resilience against infrastructure failures. We analyze application affectation and disruption rates in front of various failure patterns.

The advent of B5G/6G communication infrastructures answers to the requirements posed by emerging use cases and business models, such as Industry 4.0 and autonomous guided vehicles, which impose a set of highly demanding Key Performance Indicators (KPIs). In this regard, time-engineered applications are of particular importance. Said applications, while do not may impose very demanding latencies, they impose a strict control over them to ensure a deterministic service performance. This has given rise to the concept of Deterministic Networking that relies on adding deterministic capabilities to all involved network infrastructures. In general, optical networks are seen as one of the main enablers of latency and jitter-bounded communications, due to their intrinsic deterministic nature. In the framework of Industry 4.0 use cases, such capability can be exploited to enable remote control of optically interconnected smart factories, contributing this way to reduce both CAPEX and OPEX. However, the estimation of KPIs across optically interconnected smart factories/client domains and the provisioning of end-to-end (E2E) services arises several challenges. In this work, we present firstly multiple service provisioning schemas (single-vs multi-path) leveraging the capabilities of the Software Defined Networking (SDN) paradigm. Then, we provide tools and algorithms for best-case KPI estimation and resource allocation decision and analyze the impact of supporting heterogeneous KPIs into the obtained overall network performance.

In the 6G ecosystem, the provisioning of deterministic services with KPI guarantees over multitechnological network domains requires the proper selection of the end-to -end (E2E) paths. Since each technological domain has its own capabilities and resources, in an E2E perspective, the proper selection of the domains to achieve the service KPIs is fundamental. The paper discusses the challenges of the E2E path/domain selection, provides an overall supporting control plane architecture and presents a proof of concept.

In this paper, we address the challenge of resource allocation for time varying traffic using hit less reallocation in translucent space -division multiplexing (SDM) elastic optical networks (EON). This algorithm periodically performs hit less lightpath reallocation aiming to minimize bandwidth blocking probability while assuring uninterrupted traffic provisioning. Lightpaths are reallocated according to specific criteria, such as traffic load in next reallocation period and the number of pairs of nodes to be reconfigured. Simulations on representative network topology with semi- synthetic time varying traffic validate the effectiveness of our method across different network service types.

Several industrial use cases require the deployment of computing resources over distributed computational facilities. However, quasi-deterministic communications are a must in this scenario, arising the need for performance assurance.

Global power consumption is growing each year, with a significant contribution from the ICT sector. Although the number of primary devices, such as ROADM5 and routers, is usually constant, the number of active transceivers depends on the routing and allocation policy and can be tuned. This work demonstrates how dynamic network optimization aided by traffic prediction leads to a 16% power saving, expressed as the number of active transceivers, and a 15% provisioned traffic increase.

Internet Service Providers (ISPs) face escalating data traffic and diverse application needs, prompting the adoption of 5 G technology to enhance network agility and intelligence. However, traditional networks fall short, necessitating the integration of Network Function Virtualization (NFV), Software-Defined Networking (SDN), and Multi-access Edge Computing (MEC). Despite advancements, efficient resource allocation remains a challenge, especially with edge computing. This study evaluates Mixed Integer Linear Programming (MILP) and Heuristic algorithms for Virtual Network Embedding (VNE) in 5G networks. Results provide insights into optimal resource allocation and offloading strategies.

The provisioning of deterministic services over E2E multi-technological infrastructures requires the proper configuration of the resources to match the required Key Performance Indicators (KPI), such as latency and jitter. From an architectural point of view, such configuration is carried out by an SDN-based TSN Controller that has the responsibility to control multiple technologies. In this paper, the main challenges of the TSN controller in such scenario are deeply discussed and its role and implementation in the TIMING architecture is also discussed.

The rising of applications with intense requirements in data volumes, storage space and CPU/GPU utilization, such as Machine Learning/Artificial Intelligence (ML/AI) applications, imposes different challenges on the Data Center Network design and operation. When compared to traditional data centers infrastructures, the recent explored disaggregated optical data center concept may bring multiple benefits in terms of optimized usage of the IT and network resources. Nevertheless, at the same time, it also brings some technical challenges. In such context, the paper discusses both data and control architectural solutions for optical disaggregated data centers for ML/AI applications, focusing on their benefits but also on the associated complexities.

One of the most challenging tasks that autonomous network control systems need to face is to provision end-to-end packet flows spanning several domains. With the advent of time sensitive networking, the co-existence of services and infrastructure with and without time sensitive capabilities adds more complexity to the end-to-end flow management. In particular, avoiding unproper provisioning decisions is key to guarantee stringent quality of service requirements. In this demo, we will showcase an integrated control plane for provisioning flows spanning through domains with and without TSN capabilities. The decision making process will be supported by a digital twin in charge of estimating expected performance indicators before path provisioning acceptance.

Future 6G systems and networks will need to provide support to a wide set of highly demanding use cases. Among others, service provisioning with deterministic guarantees, such as latency and jitter must be supported in SDN-controlled networks. In an E2E scenario, the selection of the paths over the different involved technological domains must be done properly to assure the service guarantees. In this paper, the role of the Path Computation (PC) in an SDN-based control architecture for deterministic services is firstly discussed. E2E Paths selection strategies are then benchmarked.

Making optical networks more efficient and reliable requires further automation of the optical layer. In this context, we propose a closed control loop that automatically performs fine frequency adjustments of the subchannels of superchannels to maintain optimal performance despite time-dependent impairments, thus achieving three main goals. At design, our scheme reduces the need for margins and guard-bands dedicated to spectrum-related impairments such as filter or channel detunings. This allows for a more efficient network at deployment. Then, during operation, the quality of transmission (QoT) of each subchannel is maximized to make the superchannel more resilient to any kind of soft failure, thus improving network resilience. Finally, considering the elastic network paradigm, performance improvements can be converted into significant capacity upgrades. We demonstrate the ability of our solution to achieve these three goals with split-step Fourier simulations. For a four-subchannel superchannel, we demonstrate robustness to +/- 2GHz frequency detunings and gains of up to 3.7 dB in quality of transmission.

Probabilistic constellation shaping (PCS) has emerged as an advanced modulation technique that provides a fine-grained software-defined trade-off between achievable spectral efficiency (SE) and transmission reach to deliver optimal channel capacity at any distance. This paper quantifies the network throughput benefits resulting from adopting PCS in future Flex-Grid over multicore fibre optical backbone networks, compared to using traditional uniform modulation formats. In particular, different inter-channel guard band width configurations are accounted in our study, aiming to set guidelines on the technological requirements imposed to network spectrum selective switches (SSS) to take full PCS advantage in future optical backbone networks.

Many service flows between client and server end-points traverse multiple computer networks even for a private network deployment. The challenge of reducing the complexity of such services is researched on by the European Commission-funded project PREDICT-6G, with special focus on deterministic communications. The provisioning of service flows over a multi-domain multi-technology group of computer networks is a particular challenge if each domain comes with its own set of deterministic communication support. To address that, an overarching End-to-End control plane is being developed in the project and is described herein. As the proposed innovations require a mix of technologies from Standardisation Development Organisations, this paper assesses the proposition of PREDICT-6G against the ongoing work in 3GPP, IETF, ETSI and IEEE.

ML applications present time varying requirements that should be accounted for an optimized deployment. We evaluate the benefits of disaggregated DC infrastructures for supporting them and propose a dynamic re-orchestration strategy for improved resource usage.

Service quality assurance is of capital importance in modern cloud and network infrastructures, especially in multi-domain scenarios, where multiple operators collaborate to provide end-to-end (E2E) services. However, due to the dynamics of the multiple infrastructures and deployed services, it may be difficult to identify which domains need to perform (re-)configuration operations, named actuations, to keep the quality of the E2E services. In this regard, Machine Learning (ML) techniques appear as an interesting solution for guiding the actuation systems in multi-domain scenarios. With this in mind, we present a novel approach for self-optimised multi-domain service provisioning, leveraging the capacities of Deep Reinforcement Learning (DRL), with a focus on E2E service quality assurance. Running away from traditional approaches, the presented proposal tries to minimize the number of domains that need to actuate rather than determining the exact domain-specific actuations. We compare our proposal to existing strategies in terms of performance, scalability and applicability in real scenarios.

Assigning the right spectral resources is the key to flex-grid optical networking. Finding the optimal spectral allocation is a daunting task, as there are many variables at play that make accurate network models very complex. From a fiber transmission perspective, several impairments, such as amplified spontaneous emission (ASE) noise from amplifiers or nonlinear interference noise (NLIN) generated through transmission, set a limit on spectral efficiency (SE). From a transceiver perspective, modulation format, signal intensity, and spectral allocation affect the achievable capacity. On top of that, the network conditions are dynamic, causing the channel capacity to change over time. Several models have been proposed to estimate SE, ranging from the simplest to more sophisticated ones. If one is willing to sacrifice a small fraction of accuracy in exchange for a very fast service setup, some bounds can be found to guarantee a static level of service. In this work, we evaluate the degree of inaccuracy introduced by the different strategies and their complexity-accuracy trade-offs.