In intelligent transportation systems, the ability to make precise and efficient lane-changing overtaking decisions is essential for improving traffic flow, safety, and overall efficiency. However, the coexistence of both connected and non-connected vehicles, driven by the high cost of full deployment and the incomplete global adoption of standardized communication standards, has led to the emergence of Mixed Connected and Connectionless Vehicles (MCCV) scenarios. These scenarios complicate lane-changing overtaking decisions, as the unpredictability of connectionless vehicles, particularly in dense traffic, poses significant challenges and increases safety risks. Furthermore, incorporating fairness into decision-making is vital to ensure equitable treatment of all vehicles, which is key to improving road safety and traffic efficiency. To address these challenges, we propose a fairness-aware overtaking decision optimization method for MCCV scenarios, which aims to enhance fairness while improving safety and efficiency in vehicle decision-making. First, a Bayesian network-based fairness assessment method is introduced to quantify fairness under limited data conditions by modeling the probabilistic relationships between vehicle behaviors and fairness outcomes. Second, we develop a left-lane availability detection mechanism based on adaptive maneuver tree search and a fast-lane speed recommendation mechanism grounded in traffic flow analysis. These mechanisms enhance compliance with traffic regulations and improve efficiency by dynamically assessing lane conditions and providing a real-time speed recommendation based on traffic density and flow. Finally, we incorporate a K-Nearest Neighbor (KNN)-enhanced deep reinforcement learning approach, which integrates a parameterized dueling deep recurrent Q-network with KNN-enhanced experience replay. This approach effectively copes with rare but critical driving conditions, such as unpredictable vehicle behaviors or sudden traffic dynamics, improving decision-making reliability in various traffic scenarios. Extensive simulations demonstrate that the proposed method significantly enhances the fairness, efficiency, and safety of lane-changing overtaking decisions in MCCV scenarios, effectively addressing the unpredictability of mixed-vehicle interactions.

With the rapid development of artificial intelligence and the dramatic growth of communication services, the sixth-generation (6G) wireless network needs to handle communication tasks more flexibly and efficiently, significantly exacerbating the challenge of resource allocation. For the access network scenarios in 6G networks, the existing single-layer reinforcement learning resource allocation algorithms are hard to satisfy the diverse demands of users due to the complex and variable state space. Therefore, we propose a reinforcement learning-based two-timescale resource allocation scheme, aiming to jointly enhance the quality of service and system resource utilization. The proposed method comprises an upper-layer controller that allocates network resources to lower-layer controllers on a large time scale. Then, lower-layer controllers refine the resources based on user service types on a smaller time scale. To implement the proposed two-timescale allocation scheme, we propose a two-layer reinforcement learning framework consisting of a deep deterministic policy gradient (DDPG) and a dueling deep Q network (Dueling-DQN). Furthermore, recognizing that coupling multiple reinforcement learning processes may slow down algorithm convergence, we employ asynchronous training, transfer learning, and prediction-based action space simplification to expedite the model’s convergence speed. Finally, we build a prototyping network to verify the performance of the proposed small-timescale and the large-timescale allocation algorithms. Our proposed scheme demonstrates significant improvements in both resource utilization and quality of service compared to existing schemes.

With the emergence of$\mathbf{6 G}$, Inclusive Intelligent Services (IIS) are expected to become pervasive, requiring adaptive and efficient orchestration of diverse service functions. This work addresses the challenge of Service Function Chaining (SFC) provisioning in complex 6 G scenarios by proposing a hybrid framework that integrates Deep Reinforcement Learning (DRL) with a generative Conditional Variational Autoencoder (CVAE). The CVAE enhances feature representation and generalization, while the DRL agent leverages these latent features for optimized decision-making. Simulation results confirm that the proposed GenAI-SFC framework significantly outperforms state-of-the-art methods in terms of cost efficiency and end-to-end latency.

Network Function Virtualization (NFV) enables the realization of dedicated, proprietary network functions as software, which we can instantiate flexibly on commodity servers as Virtual Network Functions (VNFs). This approach facilitates significant cost reduction and operational flexibility. However, NFV also introduces new challenges, particularly regarding the availability of network services during the VNF deployment process, due to the inherently error-prone nature of software. The issue of ensuring high availability in VNF deployment has garnered considerable attention in the academic community, with redundancy provisioning commonly regarded as the standard solution. Additionally, the time-varying traffic in operator networks complicates the deployment process. Accurate traffic prediction enables operators to dynamically scale VNF instances based on demand, optimizing resource usage and reducing costs. Building on these considerations, we investigate the availabilityaware VNF deployment problem within data center networks. We incorporate a redundancy-sharing mechanism alongside traffic forecasting method to enhance resource utilization efficiency. We formally model the problem and propose an Availabilityguaranteed and Resource-efficient VNF Placement (ARVP) for mapping Service Function Chain Requests (SFCRs) in SDN/NFVenabled networks. We conduct a comprehensive numerical simulation to evaluate the performance of our proposed approach, comparing it against four alternative schemes from the existing literature. The results demonstrate that our algorithm outperforms the benchmarks regarding SFCR acceptance rate and activated nodes. Furthermore, it achieves up to 55 % resource savings when the availability requirement is six nines ($\mathbf{0. 9 9 9 9 9 9}$).

A novel fine-grained service scheduling scheme based on application-aware network (APN) for Internet of Vehicles (IoV) is proposed to achieve efficient and differentiated network resource orchestration. Application and service flow information is translated to application identifier (ID) and Sub-service ID encapsulated in IPv6 header. In this way, network can perceive various applications and provide suitable network channel resource for them. The APN-based information identifying method for application does not require unpacking of data packets and does not overly rely on network controllers, which can improve service efficiency. The experimental verification has been conducted on IoV experimental network in Xiong'an, China. In the experiment, the end-to-end delay of remote driving of APN autonomous vehicle is decreased by 67.5% compared to that of normal autonomous vehicle. The reason is that the network can identify the critical service flow sent by the APN vehicle and guide it to the dedicated channel to avoid competing for bandwidth resources in the shared channel which has 10% packet loss rate. In addition, the packet loss rate thresholds for all remote driving service flows are obtained through experiment, so that the optimal flow orchestration can be used to realize efficient utilization of network resource while ensuring smooth remote control.

Beam hopping (BH) is a widely adopted technique in multi-beam satellite communication systems, and it can effectively improve the system capacity. However, conventional BH with full frequency reuse requires spatial isolation to avoid inter-beam interference, and it will impose restrictions on the flexibility of BH and achievable system capacity. By combining the BH and multi-beam precoding, it is beneficial to satisfy the uneven traffic demands and keep the flexibility of beam management. Different from the existing works that focus on the joint BH and precoding for a single satellite, we investigate the joint multi-satellite cell association, BH pattern design and multi-beam precoding problem for dense low earth orbit (LEO) satellite communication systems. To tackle the complex problem, we decompose it into two subproblems. First, a many-to-one matching based multi-satellite cell association algorithm is proposed, which balances the load among satellites and mitigates interference among activated cells. Second, many-to-many matching based BH pattern design and quadratic transform based precoding algorithms are proposed and alternately optimized to solve the joint BH and precoding problem. Lastly, a multi-satellite joint BH and precoding algorithm based on alternating optimization (MJBHPAO) is proposed to solve the entire problem. The effectiveness of the proposed algorithm is verified with various parameters, and simulation results show that the proposed algorithm performs better than the referred schemes.

Digital twin (DT) techniques have been proposed for the autonomous operation and lifecycle management of next-generation optical networks. To fully utilize potential capacity and accommodate dynamic services, the DT must dynamically update in sync with deployed optical networks throughout their lifecycle, ensuring low-margin operation. This paper proposes a dynamic-updating DT for the lifecycle management of optical networks, employing a hybrid approach that integrates data-driven and physics-informed techniques for fiber channel modeling. This integration ensures both rapid calculation speed and high physics consistency in optical performance prediction while enabling the dynamic updating of critical physical parameters for DT. The lifecycle management of optical networks, covering accurate performance prediction at the network deployment and dynamic updating during network operation, is demonstrated through simulation in a large-scale network. Up to 100 times speedup in prediction is observed compared to classical numerical methods. In addition, the fiber Raman gain strength, amplifier frequency-dependent gain profile, and connector loss between fiber and amplifier on C and L bands can be simultaneously updated. Moreover, the dynamic-updating DT is verified on a field-trial C+L-band transmission link, achieving a maximum accuracy improvement of 1.4 dB for performance estimation post-device replacement. Overall, the dynamic-updating DT holds promise for driving the next-generation optical networks towards lifecycle autonomous management.

The rapid advancement of Software-Defined Networks (SDN) and Network Function Virtualization (NFV) has popularized the adoption of the Service Function Chain (SFC) paradigm for efficient network service delivery. This paradigm leverages the flexibility and cost-effectiveness of deploying Virtual Network Functions (VNFs) as software entities or virtual machines on off-the-shelf servers. Chaining VNFs together allows traffic to be directed through the network as required. However, existing algorithms for traffic steering and routing path computation in SFC suffer from many challenges, including complexity, lack of scalability, and low time efficiency. This paper focuses on addressing the challenges associated with VNF placement and SFC chaining in SDN/NFV-enabled networks. Our objective is to identify an optimal solution for VNF placement that maximizes the utilization of network resources. We formulate the problem as a Binary Integer Programming (BIP) model to accomplish this. Additionally, we propose a novel algorithm called DeepSelector, which incorporates deep learning techniques and an intelligent node selection network to determine the optimal placement of VNFs for SFC requests. Through performance evaluation, we demonstrate that DeepSelector achieves high network resource utilization and offers efficient VNF placement computation, significantly improving overall network performance.

Low Earth Orbit (LEO) satellite constellations have seen significant growth and functional enhancement in recent years, which integrates various capabilities like communication, navigation, and remote sensing. However, the heterogeneity of data collected by different satellites and the problems of efficient inter-satellite collaborative computation pose significant obstacles to realizing the potential of these constellations. Existing approaches struggle with data heterogeneity, varing image resolutions, and the need for efficient on-orbit model training. To address these challenges, we propose a novel decentralized PFL framework, namely,ANovel DecentraLized PersonAlized Federated Learning for HeterogeNeous LEO SatellIte CoNstEllation (ALANINE). ALANINE incorporates decentralized FL (DFL) for satellite image Super Resolution (SR), which enhances input data quality. Then it utilizes PFL to implement a personalized approach that accounts for unique characteristics of satellite data. In addition, the framework employs advanced model pruning to optimize model complexity and transmission efficiency. The framework enables efficient data acquisition and processing while improving the accuracy of PFL image processing models. Simulation results demonstrate that ALANINE exhibits superior performance in on-orbit training of SR and PFL image processing models compared to traditional centralized approaches. This novel method shows significant improvements in data acquisition efficiency, process accuracy, and model adaptability to local satellite conditions.

Although some studies in recent years have focused on the coexistence of service and task dependencies in the collaborative optimization of service caching and task offloading in Vehicle Edge Computing, the challenges brought by dual dependencies have not been fully addressed. Therefore, this paper proposes a more comprehensive joint optimization method for service caching and task offloading under dual dependencies. First, this paper proposes a service criticality prediction method based on the Gated Graph Recurrent Network to perceive complex task dependencies and accurately capture the service requirements of critical task types. Based on this, a hierarchical active-passive hybrid caching strategy is designed, which aims to satisfy diverse service demands while reducing the additional overhead caused by remote service requests. Second, a global task priority computation method based on application heterogeneity has been developed to prevent cascading delays in task chains. Finally, this paper formulates a joint optimization problem for service caching and task offloading in a three-layer VEC system, models it as a Markov Decision Process, and applies a Proximal Policy Optimization-driven collaborative optimization algorithm named COHCTO. Simulation results show that COHCTO achieves multi-objective optimization across metrics such as delay, energy consumption, caching hit rate, and application success rate under conditions different from those of other algorithms.

Large-scale transmission network (LSTN) puts forward high requirements to 6G in quality of service (QoS). In the LSTN, bounded and low delay, low packet loss rates, and controllable bandwidth are required to provide guaranteed QoS, involving techniques from the network layer and physical layer. In those techniques, routing computation is one of the fundamental problems to ensure high QoS, especially for bounded and low delay. Routing computation in LSTN researches include the routing recovery based on searching and pruning strategies, individual-component routing and fiber connections, and multi-point relaying (MRP)-based topology and routing selection. However, these schemes reduce the routing time only through simple topological pruning or linear constraints, which is unsuitable for efficient routing in LSTN with increasing scales and dynamics. In this paper, an efficient and comprehensive {routing computation algorithm namely multi-factor assessment and compression for network topologies (MC) is proposed. Multiple parameters from nodes and links in networks are jointly assessed, and topology compression for network topologies is executed based on MC to accelerate routing computation. Simulation results show that MC brings space complexity but reduces time cost of routing computation obviously. In larger network topologies, compared with classic and advanced routing algorithms, the higher performance improvement about routing computation time, the number of transmitted service, average throughput of single routing, and packet loss rates of MC-based routing algorithms are realized, which has potentials to meet the high QoS requirements in LSTN.

The emergence of Software-Defined Networks (SDN) and Network Function Virtualization (NFV) has made Service Function Chain (SFC) a popular method for delivering network services. This innovative computing and networking paradigm allows Virtual Network Functions (VNFs) to be cost-effectively deployed on a network of physical equipment flexibly and elastically. Traffic can be directed as needed by linking VNFs as an SFC. However, the current algorithms for VNF placement computation and traffic steering in SFC are often complex, unscalable, and time-consuming. This paper investigates the VNF placement and SFC chaining problem in NFV-enabled networks. To obtain the VNF placement solution that maximizes network resource utilization, we formulate the problem as a Binary Integer Programming (BIP) model. Additionally, we introduce a novel Deep Learning-based VNF Placement Algorithm (DLVPA) that uses an intelligent node selection network to place VNFs for SFC requests. Performance evaluations demonstrate that DLVPA can effectively improve network resource utilization and achieve high solution computation time efficiency.

With the emergence of Network Function Virtualization (NFV) and Software-Defined Networks (SDN), Service Function Chaining (SFC) has evolved into a popular paradigm for carrying and fulfilling network services. This new networking and computing paradigm enables virtual network functions (VNFs) to be placed in virtual machines/software entities on a network of physical devices elastically and flexibly with lower capital and operating expenditures. However, for cloud service providers, how to migrate VNFs in NFV-enabled networks for more flexible services is a critical issue that needs to be addressed. Currently, research on VNF migration mainly focuses on how to migrate a single VNF while ignoring the VNF sharing and concurrent migration. This paper assumes that each placed VNF can serve multiple SFCs. We focus on selecting the best migration location for concurrently migrating VNF instances based on actual network conditions. First, we formulate the VNF migration problem as an optimization model whose goal is to minimize the end-to-end delay of all influenced SFCs while guaranteeing network load balance after migration. Next, we design a Two-Stage Hybrid Genetic Evolution (T-SHGE) solution to solve the VNF migration problem. Finally, we combine previous experimental data to generate realistic VNF traffic patterns and evaluate the algorithm. Simulation results show that the SFC delay after migration calculated by T-SHGE is close to the optimal results and much lower than the benchmarks. In addition, it effectively guarantees the load balancing of the network after migration.

Network service providers benefit greatly from Network Function Virtualization (NFV), which allows them to outsource their Network Functions (NFs) to cloud data centers flexibly. This paper focuses on the Virtual Network Function (VNF) placement in cloud data centers while maximizing the network’s accepted Service Function Chain Requests (SFCRs). To optimize resource utilization, we consider two key factors that are often overlooked: time-varying workloads and VNF sharing based on multi-tenancy technology. We formulate the VNF placement problem as an Integer Linear Programming (ILP) model. To solve the ILP, we devise a Throughput Optimization Heuristic Solution (TOHS). Finally, we conduct a detailed numerical simulation and compare our results with contrasting schemes in the existing literature. Our evaluation shows that the performance of TOHS is near to results derived by ILP solver for small-scale problems. In addition, TOHS outperforms other solutions in various scenarios, resulting in higher network throughput and better utilization of network resources.

The Service Function Chain (SFC) has become a popular paradigm to complete mobile services due to the advancements in Mobile Edge Computing (MEC) and Network Function Virtualization (NFV). This new computing and networking paradigm allows Virtual Network Functions (VNFs) to be placed in physical devices within MEC-NFV networks cost-effectively and flexibly. However, most existing VNF placement algorithms are complex, unscalable, and time-consuming. In this paper, we investigate the VNF placement problem in MEC-NFV networks and formulate an optimization model to optimize network resource utilization. We introduce a novel Deep Learning-based VNF Placement Approach (DLVPA) that intelligently selects nodes and places VNFs for SFC requests. Performance evaluations demonstrate that DLVPA can effectively improve network resource utilization.

In the 6G vision, networks are expected to be more flexible in quickly solving network traffic scheduling issues and deploying services. Network Function Virtualization (NFV) is an innovative technology that involves extracting network functions from dedicated equipment to create Virtual Network Functions (VNFs). These VNFs are then chained together to form a Service Function Chain (SFC) that provides network service. However, there are still some issues with existing network service orchestration tools, such as unreasonable multi-traffic scheduling and additional programming requirements for end-users. We have developed a solution to address the challenges posed by data coupling and bandwidth preemption in multi-service environments. Our dynamic Quality of Service (QoS) Guarantee model utilizes hierarchical analysis to prioritize traffic among multiple service data streams and employs a service scheduling algorithm based on a weighted fair queue to allocate link resources. For user convenience, we have also created an intuitive web orchestration platform called EasyOrchestrator, enabling users to encapsulate common VNFs and build services quickly. Our experimental evaluation has shown that EasyOrchestrator significantly reduces service construction time compared to the benchmark. At the same time, our QoS assurance mechanism effectively minimizes network congestion and ensures the successful operation of high-priority services.

Virtual Network Function (VNF) placement in NTNs is challenging because Non-Terrestrial Networks (NTNs), such as satellite networks, have limited resources regarding computational power and rate. However, existing solutions do not consider satellites’ resource constraints and the bandwidth constraints of links, which are essential metrics for designing VNF placement strategies in NTNs. Utilizing Network Function Virtualization (NFV) technology to deploy related network services on satellites in VNFs is a reasonable way. This paper focuses on delay-aware VNF placement in 6G NTNs to meet the ultra-low delay requirements of different applications. In addition, we also consider how to improve the resource utilization of servers to eliminate the resource bottlenecks of resource-constrained 6G NTN facilities. Then we formulate the VNF placement problem as a weighted graph-matching problem, aiming to maximize resource utilization. We propose the Linear Programming based algorithm and the Hungarian-based algorithm to solve the VNF placement problem. Evaluation results show that our proposed solutions outperform the benchmarks regarding resource utilization and execution time.

Network Function Virtualization (NFV) is an emerging technology that extracts network functions from dedicated devices and instantiates them in the form of Virtual Network Functions (VNFs). In this paper, we focus on the multi-traffic scheduling in VNF-based service orchestration. We propose a dynamic multi-service Quality of Service (QoS) Guarantee approach, which aims to reduce data coupling between multiple services and bandwidth preemption. Then we devise a service scheduling algorithm to allocate link resources for network services. The simulation results demonstrate that our method efficiently reduces network congestion and ensures high-priority services' trouble-free running.

Enabled by Network Function Virtualization (NFV) and Software-Defined Networks (SDN), 5G networks benefit various industries (the so-called verticals) by supporting their technological and business needs flexibly and swiftly. However, a critical challenge is making high-quality joint optimal decisions for vertical demand mapping, involving Virtual Network Function (VNF) placement and optimization of network resources. In particular, to devise VNF placement schemes, network operators need to consider different objectives, such as minimizing operational costs or network latency, which are optimization objectives traditionally addressed separately. This paper studies the VNF placement for service function chains to minimize energy and traffic costs jointly. First, the problem is formulated as an optimization problem. Then we propose a joint optimization function to measure the energy consumption of physical nodes and traffic cost on links. Then, we improve the biogeography-based evolutionary algorithm to solve the proposed problem. Simulation results show that our method is effective for the proposed problem and outperforms existing methods in terms of performance.

Due to the complicated and changing urban traffic conditions and the dynamic mobility of vehicles, the network topology can rapidly change which causes the communication links between vehicles disconnected frequently, and further affects the performance of vehicular networking. To overcome this problem, we propose a intelligent multi-attribute routing scheme (MARS) for two-layered software-defined vehicle networks (SDVNs). The proposed scheme is divided into two phases, the routing path calculation and the multi-attribute vehicle autonomous routing decision-making. In this paper, we construct the topology diagram in SDVNs for finding the efficient routing paths. To increase the packet arrival rate and reduce the end-to-end delay, an intelligent multi-attribute routing scheme is proposed by employing fuzzy logic and design a technique of order preference by similarity to ideal solution (TOPSIS) algorithm to find the next-hop forwarder. To solve the uncertainty problem of multiple attributes, we apply the fuzzy logic to identify the weight of each attribute in TOPSIS algorithm. Simulation results demonstrate that MARS can effectively improve packet delivery ratio and reduce average end-to-end delay in urban environments compared with its counterparts.