Yuxi Lu

The efficient and secure management of resources within flying ad-hoc networks (FANETs) poses formidable challenges. FANETs constitute a pivotal element of the space-air–ground-integrated network (SAGIN), employing network virtualization (NV) technology in tandem with service function chain (SFC) to facilitate end-to-end network services, akin to terrestrial networks. Nonetheless, the transient, dynamic nature of FANETs coupled with their susceptibility to network attacks engenders considerable complexity in the placement of SFCs within these networks. To address the rationality and security of resource allocation for SFC placement, this article proposes a reinforcement learning algorithm that sets strict security-level restrictions on the placement process and fully extracts the key features in FANETs. Additionally, a multilayer policy network is devised to dynamically perceive alterations in the FANET environment and compute an optimal SFC placement strategy. The proposed algorithm exhibits real-time adaptability to the dynamic environment, quantifies influential factors during placement, and achieves dynamic SFC placement. To assess the efficacy of the algorithm, three evaluation metrics—namely, SFC placement success rate, long-term average revenue, and long-term revenue cost ratio—are formulated and extensively evaluated through a plethora of experiments. Comparative analysis against alternative algorithms demonstrates enhancements of 20.6%, 15.3%, and 12.1% in the aforementioned metrics, respectively. The experimental findings substantiate both the convergence and efficiency of the proposed algorithm.

In recent years, microservices, as an emerging technology in software development, have been favored by developers due to their lightweight and low-coupling features, and have been rapidly applied to the Internet of Things (IoT) and Internet of Vehicles (IoV), etc. Microservices deployed in each unit of the IoV use wireless links to transmit data, which exposes a larger attack surface, and it is precisely because of these features that the secure and efficient placement of microservices in the environment poses a serious challenge. Improving the security of all nodes in an IoV can significantly increase the service provider’s operational costs and can create security resource redundancy issues. As the application of reinforcement learning matures, it is enabling faster convergence of algorithms by designing agents, and it performs well in large-scale data environments. Inspired by this, this paper firstly models the placement network and placement behavior abstractly and sets security constraints. The environment information is fully extracted, and an asynchronous reinforcement-learning-based algorithm is designed to improve the effect of microservice placement and reduce the security redundancy based on ensuring the security requirements of microservices. The experimental results show that the algorithm proposed in this paper has good results in terms of the fit of the security index with user requirements and request acceptance rate.

In recent years, the mobile Internet and communications industry has been growing rapidly, with the Internet of Vehicles (IoVs) serving as a representative example. The emergence of software function virtualization and network function virtualization has expanded the development of IoV beyond the limitations of the traditional network. However, the increasing number of incoming nodes and the explosion of service requests pose significant challenges for data collection, transmission, and fast processing. The scheduling of service functions is a crucial issue that constrains the quality of service. While existing studies on service function scheduling in IoV are scarce, those available extensively employ linear programming and heuristic algorithms with large solution spaces, limited to offline scheduling. In this paper, we model the service function scheduling problem as a flexible shop floor scheduling problem. We integrate consideration of vehicle speed and signal strength based on the network characteristics in IoV, leverage reinforcement learning to learn high-quality scheduling rules, and employ graph neural networks to capture the complex relationship between operations and vehicle nodes, thereby realizing online scheduling. Experimental results effectively demonstrate the proposed scheduling algorithm's effectiveness and its good capability in future complex network situations.

In the context of high-speed networks with 5G and 6G, the influx of user requests under variable usage scenarios puts great pressure on the monolithic architecture, and quality of service (QoS) is gradually not guaranteed. Placing low-coupling, high-efficiency microservices on satellite edge computing nodes with wide coverage is a good solution, but the exponential increase of users and edge nodes accessing communication networks in recent years has gradually highlighted the importance of proper placement and effective management of microservices. The existing studies generally fail to achieve autonomous management of microservices in a variable and complex network environment, and the few studies on autonomous management of microservices are limited to achieving autonomous placement without constraints among microservices. The quality of service and operation cost will not be guaranteed when facing a large number of network requests at the same time. This paper addresses the much-needed problem of modeling microservice placement in satellite edge nodes as a network embedding problem and effectively captures the features that affect microservice placement performance using the attention mechanism in graph neural networks. Simulation experimental results illustrate the effectiveness of the research content of this paper for the automatic management of microservices in satellite networks, while the proposed scheme in this paper performs well in terms of success rate and the benefit-overhead ratio of microservice placement.

With the rapid development of 5G and 6G communications in recent years, there has been significant interest in space–air–ground integrated networks (SAGINs), which aim to achieve seamless all-area, all-time coverage. As a key component of SAGINs, flying ad hoc networks (FANETs) have been widely used in the agriculture and transportation sectors in recent years. Reliable communication in SAGINs requires efficient routing algorithms to support them. In this study, we analyze the unique communication architecture of FANETs in SAGINs. At the same time, existing routing protocols are presented and clustered. In addition, we review the latest research advances in routing algorithms over the last five years. Finally, we clarify the future research trends of FANET routing algorithms in SAGINs by discussing the algorithms and comparing the routing experiments with the characteristics of unmanned aerial vehicles.

Under the background of 5 G and 6 G super bandwidth and ultra-low delay, Software Defined Networking and Network Function Virtualization (SDN&NFV) makes the communication technology develop rapidly. Service Function Chain (SFC) selection is a crucial aspect of SDN&NFV, which faces many challenges of poor algorithm performance, high latency, and low profit. Moreover, most of the existing studies are stuck in optimizing for a single metric, which leads to a poor generalization of the proposed path optimization method. In this paper, we propose an SFC path optimization strategy based on the clustering of network functional layouts to address the urgent problem. First, we design a topological network identification algorithm, model the SFC path optimization problem as an attention problem and use a multi-headed attention mechanism to optimize different kinds of SFC layouts in a targeted manner. Among them, the multi-headed attention mechanism achieves the effective capture of multiple features in the SFC path selection problem compared with the traditional strategy. The experimental results demonstrate the effectiveness of Network-Functional Layout Clustering Strategy (NFLCS) in optimizing SFC paths and show the ability to optimize the service function chain paths well in future complex network situations.