Virtual network embedding (VNE) refers to the process of mapping virtual networks onto physical networks, which can improve the utilization and flexibility of network resources. However, due to the complexity of VNE problems and the requirement for network security, how to efficiently complete VNE and ensure network security is important. In this article, we analyze the characteristics of the cloud–edge–terminal collaborative vehicular network and design a resource allocation mechanism based on VNEVNE, abbreviated as DRLS-VNE. First, we establish a multidimensional heterogeneous network model and design a five-layer neural network as the deep reinforcement learning (DRL) agent. The DRL agent can adaptively extract network feature attributes, thereby improving the performance of DRLS-VNE. Second, we introduce a dynamic trust evaluation mechanism, which can evaluate the trustworthiness of each node in the virtual network in real time and set embedding constraints based on the evaluation results. Finally, we conduct experiments to verify the practicality and effectiveness of DRLS-VNE. The experimental results show that our solution can significantly enhance the performance of the VNE solution.

In the collaborative intelligent transportation system, providing precise positioning services is costly. Reducing resource consumption and improving revenue are crucial to the development of positioning services. Therefore, a practical algorithm that combines cloud and edge network environments is necessary to improve the positioning services. Integrating network function virtualization and edge computing can provide users with more flexible and efficient services. Based on the above issues, we use the service function chain (SFC) to improve the positioning services provided in cloud-edge-vehicle collaborative networks (CEVCN). We propose a deep reinforcement learning-assisted SFC embedding algorithm and improve its performance through training. We construct a five-layer policy network to sense the environment of CEVCN and derive the optimal node selection strategy. Finally, we use the breadth-first search algorithm to solve the embedding scheme for virtual links. The simulation results show that our proposed algorithm has excellent performance. The long-term average revenue is improved by 21%, the long-term average revenue-cost ratio is improved by 13%, and the embedding rate is improved by 8%.

The space-air-ground-ocean integrated network (SAGOI-Net) has become the focus of research in recent years, which has the characteristics of wide coverage and strong adaptability. However, due to the influence of multiple heterogeneous network segments, this network is unable to provide excellent quality of service (QoS). Based on the software-defined network and virtual network architecture, we abstract SAGOI-Net as a three-layer heterogeneous physical network resource, and propose a multi-domain virtual network embedding solution to optimize QoS. Specifically, before virtual network embedding, we collected SAGOI-Net's resource information through software-defined network and modeled it. In the virtual network embedding process, we first classify the virtual network request through K-means, and dynamically adjust the reward function to use reinforcement learning to solve the optimal virtual network embedding strategy. Finally, simulation experiments verify the effectiveness of the scheme.

The use of satellite technology for communication and data transmission has greatly increased in recent years, and low earth orbit (LEO) satellites have become an important part of this infrastructure. LEO satellites orbit the earth at an altitude of around 500 to 2,000 kilometers, which allows them to provide coverage to a larger area compared to higher altitude satellites. However, routing data through LEO satellites presents unique challenges due to their low altitude and the need to continuously adjust their communication links as they move. To address these challenges, various routing algorithms have been developed to optimize the transmission of data through LEO satellite networks. In this review, we will examine the different types of LEO satellite routing algorithms and their key features, as well as the challenges and opportunities they present. We will also discuss the performance and trade-offs of these algorithms and their potential applications in various scenarios.

Traditional terrestrial wireless communication networks cannot support the requirements for high-quality services for artificial intelligence applications such as smart cities. The space–air–ground-integrated network (SAGIN) could provide a solution to address this challenge. However, SAGIN is heterogeneous, time-varying, and multidimensional information sources, making it difficult for traditional network architectures to support resource allocation in large-scale complex network environments. This article proposes a service provision method based on service function chaining (SFC) to solve this problem. Network function virtualization (NFV) is essential for efficient resource allocation in SAGIN to meet the resource requirements of user service requests. We propose a federated learning (FL)-based algorithm to solve the embedding problem of SFCs in SAGIN. The algorithm considers different characteristics of nodes and resource load to balance resource consumption. Then, an SFC scheduling mechanism is proposed that allows SFC reconfiguration to reduce the service blocking rate. Simulation results show that our proposed FL-VNFE algorithm is more advantageous compared to other algorithms, with 12.9%, 2.52%, and 10.5% improvement in long-term average revenue, acceptance rate, and long-term average revenue–cost ratio, respectively.

Due to geographical factors and resource constraints, the traditional Internet architecture cannot meet the needs of the space–air–ground-integrated network (SAGIN) resource layout in the Industrial Internet of Things (IIoT) service. How to arrange network resources in SAGIN quickly and efficiently to meet the quality of service requirements of users has become a hot research topic in the industry. Based on the characteristics of SAGIN with multiple network segments, we convert the resource scheduling problem of SAGIN into a multidomain virtual network embedding (VNE) problem. This article proposes a latency-sensitive VNE algorithm based on deep reinforcement learning (DDRL-VNE) in the SAGIN environment. Unlike traditional latency optimization algorithms, we consider the effect of traffic size and hop count on latency when evaluating latency. We constructed a learning agent composed of a five-layer policy network and extracted a feature matrix as its training environment based on the network attributes of SAGIN. The node embedding is completed according to the probability that each node is embedded in the training, and then the breadth-first search strategy is used to complete the link embedding. The experimental results effectively illustrate the effectiveness of the algorithm in the SAGIN resource allocation problem.

The software-defined wide area network (SD-WAN) is considered one of the most promising paradigms for the next generation enterprise networks. However, SD-WAN users usually suffer from significant propagation delays due to the remotely deployed cloud centers. The requirements of delay-sensitive business services make the use of fog nodes and optimal resource allocation methods very important. In this paper, we propose a revenue-oriented service offloading method to improve the efficiency of SD-WAN through fog-cloud collaboration. Aiming at maximizing the service revenue, we formulate a coupled combinatorial optimization model to jointly allocate computation and communication resources in both the fog node and the cloud. To solve this problem, we propose a service offloading decision-making method based on the counterfactual regret minimization (CFR) principle according to the workload state of the fog node. This method reduces the time complexity of solving the original problem from exponential to polynomial by providing an approximate optimal solution, and achieves a good performance that is very close to the optimal solution in terms of service efficiency. Simulation results show that our method outperforms benchmark approaches in terms of both effectiveness and efficiency.

Software-defined wide area network (SD-WAN) is a new type of network architecture that has developed rapidly in recent years. SD-WAN inherits the centralized control ar-chitecture of Software-defined networking (SDN), but supports more diverse access methods and equipment types and covers a wider area. It is also associated with greater uncertainty in the network environment. These characteristics make the fault management of the SD-WAN controller more challenging, so that the existing SDN-based data backup methods cannot adapt to SD-WAN scenarios. This paper proposes an SD-WAN-oriented Distributed Data Backup and Recovery method (DDBR) based on an improved secret sharing algorithm. To deploy this method, we design an online-offline dual backup framework based on the data freshness requirements of the controller. Under this framework, dynamic data of the controller is divided into different shares, and then stored into the storage of switches. When the controller fails, data recovery can be performed on the backup controller quickly, which greatly improves the network availability. The outstanding feature of the proposed DDBR method is that it ensures the integrity and confidentiality of the backup data in an unreliable network environment, even when some of the storage nodes fail. Evaluation results on file backup example show that the proposed solution has significant advantages over existing methods in terms of backup data storage size and backup success rate.