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.

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.

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.

In space-terrestrial integrated networks, the relative motion between LEO satellites and ground terminals is inevitable, which will trigger the reassignment of the terminal IP addresses and disrupt the ongoing TCP connections. Traditional Mobile IP protocol can solve the problem by using the home agent and the tunneling mechanism. However, for space-terrestrial integrated networks, Mobile IP is inefficient as it introduces (1) increased latency when registering with the remote home agent, (2) high packet loss due to large registration latency, (3) triangular routing to the remote home agent. To address the above issues, we propose LISP-LEO, a location/identity separation-based mobility management protocol for LEO satellite networks. Specifically, (1) we divide the Earth's surface into partitions and maintain a partition-satellite mapping table in real-time according to the regularity of satellite motion, (2) we always route traffic to the satellite above the destined terminal by querying the partition-satellite mapping table, which eliminates triangular routing and the related performance overheads, (3) we handle the corner case that multiple satellites occur above the destined terminal by proposing last-hop relay. The evaluation convinces that, for the LEO-48 constellation, LISP-LEO produces a 55.0% reduction in the RTT and a 45.8% reduction in the number of forwarding hops in the worst routing case compared with Mobile IP.

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.