Ubiquitous connectivity is a key 6G usage scenario, in which large-scale sensing systems deployed in remote and underserved regions must deliver heterogeneous sensing data under stringent energy budgets and deadline constraints. This paper presents EcoPath, a two-tier data aggregation framework for clustered large-scale sensor networks. EcoPath separates low-power intra-cluster collection from a high-rate multi-interface backhaul operated by cluster heads, where Multipath QUIC (MPQUIC) can be practically deployed to exploit path diversity. At the cluster head, EcoPath jointly integrates (i) a deadline-aware bundling controller that aggregates sensor frames into MTU-bounded bundles to amortize protocol overhead while bounding additional waiting time, and (ii) a robust multi-path scheduler that prioritizes packets using Weighted Earliest- Deadline-First (W-EDF) with fairness protection and selects backhaul paths via a stability-aware quality metric with hysteresis to avoid flapping under time-varying links. We further formulate an explicit energy–timeliness optimization and show how its outputs parameterize the online bundling and scheduling policies. Extensive simulations with realistic wireless effects, together with baselines and ablations, demonstrate that EcoPath improves energy efficiency and deadline satisfaction for large-scale aggregation.

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

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.

Network Function Virtualization (NFV) is a promising paradigm that separates network functions from proprietary devices. Network service in NFV-enabled networks is achieved as a Service Function Chain (SFC), consisting of a series of ordered Virtual Network Functions (VNFs). However, migration of VNFs for more flexible services within dynamic networks is a key challenge. Current VNF migration studies mainly focus on single VNF migration decisions without considering the sharing and concurrent migration of VNF Instance (VNFI). In this paper, we assume that each deployed VNFI is used by multiple SFCs and deal with the optimal location allocation for the concurrent migration of VNFIs based on the actual network situation. We first formalize the VNF migration and SFC reconfiguration problem as a mathematical model, which aims to minimize the end-to-end delay for all affected services and to guarantee network load balancing after the migration simultaneously. To this end, we prove the NP-hardness of this problem and propose the Improved Hybrid Genetic Evolution (IHGE) algorithm to address it. Besides, to reduce the computation overhead of IHGE for large-scale networks, a multi-stage heuristic algorithm based on optimal order (MSH-OR) is designed. Finally, we perform a side-by-side comparison with prior algorithms. Extensive evaluation shows that the proposed approaches can effectively reduce the average delay for different scale networks while ensuring network load balancing.

Since the advent of network function virtualization (NFV), cloud service providers (CSPs) can implement traditional dedicated network devices as software and flexibly instantiate network functions (NFs) on common off-the-shelf servers. NFV technology enables CSPs to deploy their NFs to a cloud data center in the form of virtual network functions (VNFs) without costly capital expenditures and operating expenses. However, it is an essential but intractable issue for CSPs to devise a suitable VNF placement scheme to optimize network resource consumption and improve network performance. In this article, we focus on the VNF placement problem for mapping users’ service function chain requests (SFCRs) in cloud networks. To enhance network resource utilization, we consider the fundamental resource overheads and implementation method of VNFs. The VNF placement problem is formulated as an integer linear programming model with the aim of minimizing the total network resource consumption while guaranteeing the delay requirements of SFCRs. We devise a two-phase optimization solution (TPOS) to solve the problem. TPOS contains a mapping phase to map SFCRs on servers and an adjustment phase to optimize the placement of VNFs and VNF requests. Evaluation results demonstrate that TPOS can derive near-optimal server resource consumption and significantly enhance network resource utilization. TPOS can guarantee the delay requirements of SFCRs and outperform contrastive schemes in terms of activated servers, SFCR acceptance ratio, and average VNF utilization.

Nowadays, network softwareization is an emerging techno-economic transformation trend that significantly impacts how enterprises deploy their network services. As an essential technology in this trend, Network Function Virtualization (NFV) enables scalable and inexpensive network services by flexibly instantiating Virtualized Network Functions (VNFs) on commercial-off-the-shelf devices. In this paper, we focus on the VNF placement problem in NFV-enabled networks, aiming to maximize the number of accepted Service Function Chain Requests (SFCRs) while guaranteeing their delay requirements. To improve resource utilization efficiency, we take account of Fundamental Resource Overheads (FROs) and the shareability of VNF instances. We mathematically formulate the VNF placement problem and propose the Throughput Optimization and Delay Guarantee (TO-DG) heuristic solution, consisting of an affinity-based SFCR mapping algorithm and a VNF request adjustment algorithm. The evaluation results show that the performance of TO-DG is near to results derived by ILP solver for small scale problems. Moreover, TO-DG obtains higher network throughput than contrasting schemes in different scenarios and significantly improves network resource utilization.

Network Function Virtualization (NFV) enables cloud service providers (CSPs) to flexibly place their network functions on common off-the-shelf servers in the form of virtual network functions (VNF), without incurring costly capital and operating expenses (CAPEX/OPEX). In the NFV-enabled network, service function chains (SFCs) are responsible for accomplishing users' service requests by steering traffic through a set of VNFs in a specified order. Therefore, it is an important but intractable issue for CSPs to devise an optimal VNF placement scheme to enhance network performance and profits. In this paper, we focus on the VNF placement problem for mapping SFC requests (SFCRs) in NFV-enabled networks, considering the delay requirement of SFCRs. To improve resource utilization, we consider the basic resource overheads and sharability of VNF instances. Then we formulate the problem as an integer linear programming (ILP) model, with the purpose of total resource consumption minimization. Afterward, the novel SFCR mapping algorithm (SMA) and VNF request adjustment algorithm (VAA) are proposed to map SFCRs and optimize the placement of VNF requests. Simulation results show that our approach is near-optimal in terms of node resource consumption. Besides, it provides higher performance in terms of node resource consumption, average VNF utilization and the number of activated servers compared with the benchmarks.

Mobile Edge Computing (MEC) is an emerging architecture that supports ultra-low latency and high-bandwidth services by deploying servers at the edge of the network to provide computing and storage resources. Recent studies tend to combine (Network Function Virtualization) NFV with MEC and deploy (Virtualized Network Functions) VNFs on MEC servers to achieve fast access to the edge user equipment (UE). However, to guarantee the QoS requirements of mobile users, it is necessary to migrate VNFs to an advisable edge server when users move across Base Stations (BS). How to choose the target BS for VNFs migration? How to select the path for VNF data migration? How to ensure the QoS of user services during the migration process? To solve these issues, we study the seamless VNFs migration problem in mobile edge networks and formulate it as an ILP model, which aims to minimize the migration delay and cost. Then we propose a migration algorithm based on Dijkstra (MBD) to obtain the migration destination BS and migration paths. We implement the mathematical model in Gurobi and design a Greedy algorithm to compare the performance with the MBD algorithm. The experiment results show the effectiveness and efficiency of our algorithm.

Network function virtualization (NFV) and mobile edge computing (MEC) enable internet service providers (ISPs) to deploy service function chains (SFCs) to achieve the convenience and performance benefit without incurring high service delay, capital expenditures, and operating expenses. In MEC-NFV networks, network services are deployed in the form of service function chains (SFCs), each consisting of an ordered set of virtual network functions (VNFs). In this paper, we focus on the VNF placement problem in MEC-NFV enabled networks, aiming to optimize the throughput of SFC requests (SFCRs). First, we involve the sharing mechanism of VNF instances in the problem formulations, which can improve network resource utilization and save more node resources. Then we formulate the problem mathematically and propose a correlation-based mapping algorithm to map SFCRs in the network. Moreover, we design an adjustment algorithm to optimize the mapped SFCRs. Evaluation results show that our proposed solution efficiently improves the throughput of SFCRs compared with the benchmarks.

With the increasing scale of cloud datacenters and rapid development of virtualization technologies, many cloud-based services have been deployed to meet requirements. Virtual machines (VMs) are placed on physical servers, and often provide virtual environment for cloud services. Therefore, virtual machines placement (VMP) problem has gradually attracted many attentions. It is meaningful that how to effectively and efficiently place VMs on servers to guarantee the service reliability and reduce the bandwidth consumption. In this paper, we first formulate VMP with a reliability model and a bandwidth consumption model, and analyse its complexity. Then we propose a VMP optimization approach to solve the problem and prove its effectiveness and efficiency. The core algorithm of our approach is an approximation algorithm to get VM partitions under the constraint of a specified reliability parameter. Then placement problem is transformed into matching problem between VM partitions with physical servers. Finally, the evaluation results show the effectiveness of the proposed approach and performance advancement over the existing approaches.

Network Function Virtualization (NFV) brings great flexibility and scalability to the deployment of network services by decoupling network functions from dedicated devices, which has attracted more attention from both academia and industry. Network services in NFV are deployed in the form of Service Function Chain (SFC), which consists of multiple ordered Virtual Network Functions (VNFs). However, how to effectively place VNFs remains a problem to be solved. In this paper, we investigate joint correlation-aware VNF selection and placement problem. We first formulate the problem as an Integer Linear Programming (ILP) problem and propose a method based on self-learning matrix to partition VNF correlation. Then, we design a Joint Correlation-aware VNF Placement (JCVP) algorithm based on Dynamic Programming to transform the problem into several VNF mapping subproblems. Extensive simulation results show that compared with the previous algorithms our approach has better performance in link occupancy, SFC acceptance, and VNF utilization rate.

Although network function virtualization (NFV) is a promising approach for providing elastic network functions, it faces several challenges. A critical but difficult issue for the service and network providers is deciding where to instantiate a list of virtual network functions (VNFs), namely VNF placement problem. In this paper, we investigate the VNF placement, for the purpose of resource and network traffic consumption minimization. Moreover, we consider the arrival rates of users' requests for different types of service function chains (SFCs). This allows the placement scheme to adapt to users' time-varying requests and improve the network resource utilization. Then we formulate the VNF placement problem as a jointly constrained optimization problem. Afterwards, we propose an approach called joint optimization resource and traffic consumption (JORTC) with enhanced biogeography-based (EBBO) optimization algorithm to resolve the VNF placement problem. Finally, the evaluation results show the effectiveness of J-ORTC approach and performance advancement over the benchmarks.

Network Function Virtualization (NFV) has emerged as an innovative and promising network architecture which can migrate Network Functions (NFs) from costly physical equipment to dynamically allocated virtualized instances. Using these Virtual Network Functions (VNFs), many end-users can chain VNFs together to create network services which are commonly referred to as Service Function Chains (SFCs). An SFC involves multiple VNFs and describes how they interact, as incorrect dependencies between any of these VNFs may cause packets forwarding errors. However, few orchestration tools are equipped with a verification mechanism to check SFC before deployment. In addition, most existing NFV orchestration tools are over complicated, making it difficult for end-users to learn and use. In this paper, we introduce a model called SFC-Verifier (SFC-V) which targets on automatically detecting the constraints between VNFs, helping end-users compose and verify SFCs in service design phase. Built on SFC-V, we present EasyOrchestrator which facilitates end-users to create and deploy network service. Users can develop customizable network services via a UI-friendly environment on a web browser. The evaluation results demonstrate that the SFC verification time and response time of EasyOrchestrator are much smaller than the benchmarks. Besides, it effectively reduces the end-users' service development time and improves service development accuracy.

Network Function Virtualization (NFV) is an emerging technology which can migrate Network Functions (NFs) from costly hardware to dynamically allocated virtualized instances. Using these Virtual Network Functions (VNFs), many nonprofessional people can chain VNFs together to create network services. However, most existing NFV orchestration tools are not equipped with a verification mechanism to check service function chain (SFC) before deployment. In addition, most of orchestration tools are complicated, making it difficult for end-users to learn and use. In this paper, we present a network service creation platform with automatic verification mechanism, called EasyOrchestrator. It is designed to automatically detect the dependencies and conflicts between VNFs, thus to help end-users compose and verify SFCs in design phase. It also provides end-users with a design-as-development network service creation environment.

With the emergency of IoT, there are many IoT network slices with different network requirements. Most of the current IoT system are specific and non-programmable and therefore their slices are difficult to reuse. It is difficult to meet different QoS requirements especially in IoT system because there are plenty of IoT sensors in IoT system. In this paper, we propose a novel IoT network slicing creation system which based on two emerging SDN and NFV technologies. It provides an easily-operating service creation environment and a service execution environment based on micro service architecture. We implement an IoT muti-flow transmission scenario. After adding subservices and QoS policies into a business process at the design plane, the IoT scenario can run automatically at the execution plane. Experiment results on the scenario show that the numbers of packets per second of different flows are changing gradually depend on QoS policies.