Edge–cloud collaboration integrated with network virtualization is indispensable for diversified edge services. Meanwhile, the multilayer elastic optical network (ML-EON) is a promising underlying network for virtual network requests (VNRs) customized for edge–cloud collaborative services. However, the joint allocation of computing resources and high-dimensional ML-EON resources in virtual network embedding (VNE) will pose great computational complexity for online service deployment. In this article, we propose an approximately lossless model compression mechanism to ease the computing burden of the VNE over ML-EON for edge–cloud collaborative services. An integer quadratic constraint programming (IQCP) model is established for the problem. Model compression based on virtual link mapping cost estimation (VLMCE) is investigated to shield the variables and constraints related to ML-EON. In particular, the resource metric and topology metric are introduced into VLMCE to cope with resource contentions among virtual links in the same VNR, and improve estimation accuracy. The model solving relies on Hopfield neural network (HNN) is further studied, where optimizing the compressed model is losslessly converted to minimizing the energy function of HNN. The experimental results reveal that the proposed mechanism guarantees an approximately lossless algorithm performance and a high-time efficiency compared with the original IQCP model. The performances of VNR cost and blocking ratio are also promoted compared with the benchmarks.

The commercialization of 5G and the explosive emergence of new applications stimulate the exponential growth of network traffic and diversification of services. It is promising to integrate multi-band elastic optical network (MBEON) and network virtualization for large volume traffic transmission and highly diverse services. However, performing virtual network embedding (VNE) for network virtualization over MBEON faces the challenge of severe inter-channel stimulated Raman scattering (ISRS) effect, which complicates the underlying physical layer effect in the substrate network. In this paper, we investigate the ISRS-aware VNE over MBEON, where a cross-matching mechanism is proposed for virtual node mapping (VNM) and a hypergraph is introduced for parallel virtual link mapping (VLM). A lightpath-level integer linear programming model is first formulated. To integrate the cost and availability of VLM, which significantly affect the performance of the VNE under the ISRS effect, into the VNM process, the “virtual node-substrate node” mapping pairs are specifically evaluated through the cross-matching mechanism. Moreover, to tackle the couplings among multiple lightpaths induced by the wide spectrum ISRS effect, hypergraphs are used to model the ISRS effect-aware quality of transmission (QoT) constraints among multiple lightpaths. A hypergraph maximal weight independent set heuristic is presented for lightpath selection, which guarantees the obedience of basic constraints and generates near-optimal solutions. Experimental results show that the proposed methods decrease blocking ratio by more than 30% compared with the benchmarks with similar computational complexity.

With the explosive growth of edge applications in the 5G/B5G era, edge-cloud collaboration (ECC) is playing a prominent role in edge service provisioning. For highly diversified edge-cloud collaborative services (ECSs), the joint allocation of heterogeneous computing resources in heteroge-neous servers and multi-dimensional underlying optical network resources should be conducted. In this paper, we investigate the heterogeneous task scheduling for ECSs over multilayer elastic optical network (ML-EON), which involves the joint allocation of heterogeneous computing resources in edge and cloud servers and high-dimensional network resources. We propose a Task-Node Matching Score (TNMS) based method, which evaluates the fitness for each mapping tuple between each task in ECS and each substrate node in ML-EON, and adaptively generates a specific matching score for each task-node pair. Furthermore, TNMS is extended with a pre-allocation mechanism (TNMS-Pre) to estimate the costs of multi-dimensional resources in ML-EON for virtual link (VL) mapping. The estimated VL mapping costs are integrated into the matching scores to guide the task placement to be cost-efficient. To guarantee the feasibility, a maximal weight matching (MWM) based method is presented to determine the task placement schemes. Simulation results demonstrate the effectiveness of the adaptive scoring for heterogeneous task placement and the pre-allocation mechanism for reducing the ML-EON costs.

The intelligent service function chains (SFCs) provisioning is of great significance for agile deployments of 5G vertical applications. However, the heterogeneities of entities in substrate network (SNet) and SFCs hinder deep learning (DL) models to fully integrate the information of SNet and SFCs. Furthermore, the potential infeasibility of output policies and difficulty in training data acquisition also pose challenges to DL methods. To overcome the above limitations, we propose a Differentiated Weight Graph Convolutional Neural Network (DWGCN) model, which configures different weights for different kinds of entities, to predict the optimal virtual network function (VNF) placements. Moreover, the model is integrated with maximal weight matching to enhance the feasibility of VNF placement policies. A transfer learning method is further introduced to reduce the required training data with knowledge transfer. Experimental results demonstrate the effectiveness of the proposed methods in SFC mapping cost, high time efficiency, and knowledge transferability.