Future 6G services will require strict performance guarantees, especially in terms of delay, end-to-end (e2e) across multiple network domains including packet and radio segments. While deterministic transport and slice-based capacity allocation can improve segment-level performance, ensuring e2e Network Service (NS) performance remains challenging as it requires making decisions Near–Real-Time (Near-RT) on a per-service basis, which does not fit well within the typical centralized control and orchestration hierarchy. Multi-agent systems (MAS), where a number of distributed agents collaborate, has demonstrated its capabilities for such Near-RT control. Agents equipped with Deep Reinforcement Learning (DRL) engines autonomously made traffic routing decisions based on e2e telemetry measurements. In this paper, we extend such MAS solutions for NS traffic routing focused on covering several issues that appear under frequent NS reconfiguration, e.g., caused by end device mobility. In addition, we define a lifecycle for NS operation that includes the initial MAS deployment, model reconfiguration during operation, and NS reconfiguration. The proposed lifecycle requires the definition of DRL training and validation procedures to produce models ready to be deployed with guaranteed performance under certain network conditions. In addition, model selection algorithms are defined for the lifecycle scenarios. In case of NS reconfiguration, a procedure for probe testing the actual network conditions is proposed to improve model selection. Evaluation across a meaningful set of network and traffic scenarios shows that the MAS is able to maintain e2e delay guarantees under all the lifecycle scenarios.

Time-Sensitive Networking (TSN) standards provide scheduling and traffic shaping mechanisms to ensure the coexistence of Time-Sensitive (TS) and non-TS traffic classes on the same network infrastructure. Nonetheless, much effort is still needed on the operation of such TSN capable network infrastructure to ensure that the required performance of the different flows, defined in terms of key performance indicators, can be met once the flows are deployed in the network. In this paper, we focus on such aspects and propose a solution involving network-wide scheduling for TS flows, as well as performance estimation for non-TS flows. Specifically, a control plane architecture especially designed for provisioning TS and non-TS flows is proposed. The architecture integrates: i) a TS Flow Scheduler Planner for defining the scheduling of requested TS flows along a path so as to meet their required performance; and ii) a Network Digital Twin to estimate the performance of requested and already established non-TS flows. Differently from standardized time-aware schedulers, per-TS flow queues are assumed so as to guarantee minimal jitter. Efficient algorithms are proposed so the provisioning of flows can be carried out with high accuracy and short time. Simulation results for heterogeneous scenarios demonstrate the feasibility and efficiency of the proposed control plane architecture, as well as point out the limitations of current time-synchronization mechanisms when high-speed interfaces are considered.

The deployment of beyond 5G and 6G network infrastructures will enable highly dynamic services requiring stringent Quality of Service (QoS). Supporting such combinations in today’s transport networks will require high flexibility and automation to operate near real-time and reduce overprovisioning. Many solutions for autonomous network operation based on Machine Learning require a global network view, and thus need to be deployed at the Software-Defined Networking (SDN) controller. In consequence, these solutions require implementing control loops, where algorithms running in the controller use telemetry measurements collected at the data plane to make decisions that need to be applied at the data plane. Such control loops fit well for provisioning and failure management purposes, but not for near real-time operation because of their long response times. In this paper, we propose a distributed approach for autonomous near-real-time flow routing with QoS assurance. Our solution brings intelligence closer to the data plane to reduce response times; it is based on the combined application of Deep Reinforcement Learning (DRL) and Multi-Agent Systems (MAS) to create a distributed collaborative network control plane. Node agents ensure QoS of traffic flows, specifically end-to-end delay, while minimizing routing costs by making distributed routing decisions. Algorithms in the centralized network controller provide the agents with the set of routes that can be used for each traffic flow and give freedom to the agents to use them during operation. Results show that the proposed solution is able to ensure end-to-end delay under the desired maximum and greatly reduce routing costs. This performance is achieved in dynamic scenarios without previous knowledge of the traffic profile or the background traffic, for single domain and multidomain networks.

The massive deployment of 5G and beyond will require high capacity and low latency connectivity services, so network operators will have either to overprovision capacity in their transport networks or to upgrade the optical network controllers to make decisions nearly in real time; both solutions entail high capital and operational expenditures. A different approach could be to move the decision making toward the nodes and subsystems, so they can adapt dynamically the capacity to the actual needs and thus reduce operational costs in terms of energy consumption. To achieve this, several technological challenges need to be addressed. In this paper, we focus on the autonomous operation of Digital Subcarrier Multiplexing (DSCM) systems, which enable the transmission of multiple and independent subcarriers (SC). Herein, we present several solutions enabling the autonomous DSCM operation, including: i) SC quality of transmission estimation; ii) autonomous SC operation at the transmitter side and blind SC configuration recognition at the receiver side; and iii) intent-based capacity management implemented through Reinforcement Learning. We provide useful guidelines for the application of autonomous SC management supported by the extensive results presented.

The performance of optical devices can degrade because of aging and external causes like, for example, temperature variations. Such degradation might start with a low impact on the Quality of Transmission (QoT) of the supported lightpaths (soft-failure). However, it can degenerate into a hard-failure if the device itself is not repaired or replaced, or if an external cause responsible for the degradation is not properly addressed. In this work, we propose comparing the QoT measured in the transponders with the one estimated using a QoT tool. Those deviations can be explained by changes in the value of input parameters of the QoT model representing the optical devices, like noise figure in optical amplifiers and reduced Optical Signal to Noise Ratio in the Wavelength Selective Switches. By applying reverse engineering, the value of those modeling parameters can be estimated as a function of the observed QoT of the lightpaths. Experiments reveal high accuracy estimation of modeling parameters, and results obtained by simulation show large anticipation of soft-failure detection and localization, as well as accurate identification of degradations before they have a major impact on the network.