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.

The growing number of services and users leads to an increasing scarcity of spectrum resource and brings challenges to terrestrial-satellite networks (TSNs). For the limited spectrum resource and long-distance transmission characteristics, the improvement of spectrum efficiency is particularly important in TSNs. In this paper, we discuss the optimization problem of spectrum resource utilization in TSNs. By establishing a rate-split multiple-access and multiple-input-single-output (MISO) based terrestrial-satellite network architecture, a joint optimization scheme of user association, power and rate allocation is proposed. The objective of maximizing the system spectrum efficiency can be obtained, so as to realize the efficient utilization of spectrum resource while guaranteeing the communication requirements of users.

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 ($\mathbf{0. 9 9 9 9 9 9}$).

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.

The global expansion of 5G networks has led to a significant increase in network traffic. In data centers, virtual machines (VMs) must be allocated on Physical Machines (PMs) according to a specific topology. Each VM requires specific network resources to function correctly. Consolidated VM deployment can help reduce traffic consumption and prevent bandwidth-related bottlenecks, while loose deployment can minimize VM failure rates and guarantee availability during PM and switch failures. A reasonable VM deployment plan is vital to improve availability and minimize network bandwidth consumption. This paper presents four typical data center architectures, network topologies, and cost matrices extending to generality. A joint optimization model is proposed to measure Virtual Cluster (VC) risk with global availability constraints. A heuristic algorithm is then introduced to minimize the value of the constrained optimization function. The evaluation results indicate that the proposed method is effective and improves performance over the benchmarks.

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.