Designing a robust, resilient, and cost-efficient Fronthaul is essential to meet the ultra-reliability and high-bandwidth demands of 5G and beyond radio access networks (RANs). State-of-the-art probabilistic backup design approaches that rely on the likelihood of link failures rather than assuming worst-case scenarios to avoid over-provisioning are realistic and cost-effective. However, tackling this design problem is challenging due to its inherent stochasticity. Traditional solutions, such as robust optimization, tend to overestimate backup requirements, leading to inflated costs. In addition, existing transport technologies deployed in mobile network operators (MNOs) Fronthaul are not flexible and scalable to new 5G requirements. To address this, we propose a novel stochastic Fronthaul backup design model that leverages the combined advantages of FlexEthernet (FlexE) and elastic optical networks (EONs) to reduce bandwidth usage, backup capacity, and overall costs. By applying Chernoff bounds, we reformulate the stochastic model into a non-convex optimization problem and develop CBFH, a successive convex approximation algorithm tailored for single-MNO scenarios. For multi-MNO environments, we introduce CBFHA, an approximation algorithm based on the facility location problem. Experimental results demonstrate that our approach reduces backup costs by at least 48.92% compared to existing state-of-the-art methods.

Without network slicing supports, traditional Fronthaul architectures struggle to meet the demanding requirements of 5G networks, such as the ultra-low latency and high bit rate specified by the enhanced common public radio interface (eCPRI). In this paper, we design a novel Fronthaul architecture that leverages FlexEthernet (FlexE) over elastic optical network (EON) to enable Fronthaul slicing meeting 5G Fronthaul requirements. Our Fronthaul design is optimized by an integer linear programming (ILP) model, named eFFP, that minimizes the total cost of ownership (TCO). While eFFP meets the strict Fronthaul requirements by provisioning network resources based on worst-case traffic load, it tends to overestimate required bit rate as a result of the inherent uncertainty and variability in real-world traffic. To tackle this challenge, we introduce uFFP, a stochastic Fronthaul provisioning strategy tailored to accommodate uncertain traffic demands and mitigate expenditure wastage. Relying on historical data, uFFP assesses statistical characteristics of traffic patterns to better estimate Fronthaul bit rate. Subsequently, we employ chance-constrained optimization to reformulate the uFFP problem, which is approximately solved using a convex relaxation approach known as uFFPA, and optimally solved using a deep reinforcement learning (DRL) approach called uFFPL. Simulation results demonstrate that our proposed solutions achieve significant cost savings, reducing TCO by 39.79% compared to the baseline.

Traditional FlexEthernet (FlexE) defragmentation schemes have successfully been employed to reallocate the slots of affected FlexE clients during network changes such as FlexE physical link (PHY) failures in multi-layer multidomain (MLMD) networks in the context of fixed traffic rate. In such a context, constant slots of FlexE clients are statically pre-assigned using a round-robin algorithm before the network change takes place. However, in more realistic scenarios where traffic varies over time, this static assignment requires multiple defragmentation steps, potentially violating the maximum tolerated reconfiguration time and resulting in traffic loss. Therefore, a dynamic defragmentation scheme is required to efficiently move the affected slots without disrupting unaffected traffic. This paper introduces FDL, a semi-supervised learning approach designed to efficiently address the FlexE defragmentation problem under time-varying traffic conditions. FDL leverages an autoencoder for unsupervised pre-training, particularly due to the considerable amount of unlabeled data resulting from the unsolvable high-complexity optimization problem. To optimize throughput while adhering to reconfiguration time deadlines, FDL employs a gated recurrent unit (GRU) structure to forecast the future reassignment of FlexE clients’ slots over the defragmentation steps ahead. Simulation results demonstrate that the proposed FDL achieves a throughput that is 17.18% higher than a state-of-the-art approach.

Restoring traffic in multi-layer multi-domain networks (MLMD) can be inefficient and expensive due to the reconfiguration of both intra-domain and inter-domain paths under limited resources and information sharing. This often results in traffic loss and resource over-provisioning within the MLMD, leading to sub-optimal restoration throughput and high costs. In this study, we harness FlexEthernet (FlexE) on inter-domain links to maximize the restoration throughput at minimum cost. FlexE link aggregation is an effective technique to deal with the costly impact of alternative domain rerouting that allows diverting traffic over aggregated links parallel to the failed ones, without disrupting the intra-domain connections. Additionally, FlexE helps increase network reutilization by leveraging time division multiplexing (TDM) to flexibly shift affected traffic to underutilized aggregated links. However, scheduling traffic migration in FlexE is a challenging issue that has not been fully investigated in the literature. In this paper, we initially formulate the FlexE-based traffic restoration problem as a mixed integer non-linear program (MINLP) and then introduce an approximation algorithm to efficiently solve this problem in polynomial time. Furthermore, we propose a supervised learning approach to predict the optimal restoration policy for large-size instances. Experimental results show that our solution restores up to 14% more traffic than a state-of-the-art approach.

Traditional Fronthaul architectures are not efficiently provisioned for 5G due to their lack of Fronthaul slicing supports or/and inability to satisfy the very strict latency and high bandwidth requirements defined for the enhanced common public radio interface (eCPRI). In this paper, we propose a new Fronthaul architecture that leverages Flex-Ethernet (FlexE) to guarantee 5G Fronthaul quality of service (QoS) requirements without over-provisioning the Fronthaul resources. By separating the MAC and PHY layers through a time division multiplexing (TDM) shim, FlexE allows for efficient aggregation of huge traffic volume and the design of low-latency hard network slicing architectures. Unfortunately, no standard has been defined for eCPRI transmission support over FlexE. Therefore, we propose a new protocol stack in which FlexE clients are efficiently allocated to carry eCPRI data of different 5G slices. We then formulate the FlexE-based Fronthaul provisioning optimization problem as an integer linear program (ILP) model. Simulation results show the proposed solution saves 82% of CAPEX and 94% of OPEX compared to a Fronthaul baseline.

Recently, Flexible Ethernet (FlexE) has emerged as a new transmission technology allowing the flexible utilization of optical transport. This flexibility helps improve network ability against failures. FlexE recovers from a physical link (PHY) failure by migrating traffic to a new PHY. However, this task is costly, especially for critical failures or when the network is under high utilization. In this paper, we investigate the FlexE Traffic Restoration (FTR) problem that aims to maintain high network utilization by the fast recovery of FlexE clients with the minimum cost using the spare capacity in the already deployed PHYs. High network utilization can be obtained by rerouting of FlexE subgroups, moving clients to another subgroup, and shifting the clients’ slots in the same subgroup without traffic disruption. We formulate the FTR optimization problem and solve it in polynomial time using learning theory and approximation. Experiments carried out in a real testbed show the proposed solution recovers 63% more traffic than baseline restoration schemes.

Optimized routing in multi-layer multi-domain (MLMD) IP-optical networks is challenging due to different technologies and policies in different domains. In this paper, we investigate the problem of using the hierarchical path computation engine (PCE) to leverage the performance of FlexE-the new flexible Ethernet technology which is used to map traffic between different layers and different domains. Our proposed PCE can be implemented in MLMD orchestration platforms to optimize network utilization while meeting delay constraints. We formulate the optimization problems of traffic routing and physical slot assignment for both FlexE-Aware and FlexE-Unaware modes with respect to QoS requirements, intra-domain information privacy and FlexE constraints. To solve the problem, we propose new algorithms that jointly optimize the routing and FlexE client assignment in polynomial time. To deal with the issue of missing intra-domain information, we use a novel implicit routing strategy to collect the intra-domain information from the child PCEs. Simulation results show the proposed solution achieves 90.1% higher efficiency than the state-of-the-art solutions.

A main component of the Multi-Layer Multi-domain (MLMD) orchestration is the end-to-end path computation over the packet and optical layers. Routing in MLMD networks is complex and requires special computational elements and cooperation between different layers and domains. The goal is to optimize the utilization of Wide Area Networks (WANs) leveraging on FlexE - the new Flexible Ethernet technology which couldn’t be fully achieved from local resource allocation in a single domain. We present MLMD-PCE, a path computation engine for MLMD networks that achieves optimal routes through a hierarchical path computation. We formulate an optimization problem of traffic routing and resource assignment for FlexE-Aware and FlexE-Unaware modes and propose an approximation algorithm that runs in polynomial time. Another issue of multi-domain routing is the lack of visibility over the intra-domain typologies in the parent-PCE. To solve this problem, we use a novel mechanism to gather the intra-domain information from the child-PCEs while keeping the domain privacy. Simulation results show that MLMD-PCE carries 77% more traffic than the current Hierarchical-PCE.