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Through the integration of Network Function Virtualization (NFV), Software-Defined Networks, cloud service providers can realize cost reduction, performance enhancement, and support for advanced application scenarios. Within this framework, Service Function Chain (SFC) has emerged as a pivotal approach for delivering network services, offering flexibility and cost-effectiveness in provisioning Virtual Network Functions (VNFs) dynamically and elastically across a network of physical devices. This paper concentrates on optimizing VNF placement within NFV-enabled networks to elevate the number of SFC Requests (SFCRs) successfully processed. Emphasizing the often-overlooked factors of time-varying workloads and VNF sharing via multi-tenancy technology, we present a novel Integer Linear Programming (ILP) formulation for the strategic placement of VNFs. To effectively solve this ILP, we introduce a Throughput Optimization Heuristic Solution (TOHS), comprising an algorithm based on correlation for assigning SFCRs to nodes, alongside a VNFR aggregation algorithm aimed at optimizing node resource utilization by consolidating VNFRs requiring identical VNF types. Through detailed numerical simulations and comparative analysis with existing literature, we demonstrate that TOHS approaches the performance of ILP solvers for small-scale problems, outperforming other solutions in diverse scenarios. Specifically, TOHS achieves higher network throughput and more efficient utilization of network resources, underscoring its efficacy in practical NFV-enabled network deployments. [ABSTRACT FROM AUTHOR]

Network function virtualization is a key enabling technology for the customization of network services in next-generation networks to support diverse applications. Most enterprise and network services contain specific network functions that are stitched together in a predefined sequence to form a service function chain. The deployment and scheduling of a service function chain onto the substrate network play a vital role in deciding the efficiency of resource utilization and the performance of network management. For a delay-sensitive network service request traversing a service function chain, the end-to-end packet delay is a crucial parameter that indicates the deployment performance. Transmission, propagation, processing, edge queueing, and virtualization delays all impact the order in which virtual network functions execute. Service level agreement violations and incorrect schedules are produced when the controller does not take edge queueing and virtualization delays into account. In this work, we propose a service function chain scheduling problem for the optimization of the end-to-end delay while considering transmission, propagation, queueing, virtualization, and processing delays. Then, we propose a scheduling approach based on the earliest finish times of the physical machines to minimize the end-to-end delay of the service function chain. The performance of the proposed service function chain scheduling approach using the earliest finish time is evaluated in terms of end-to-end delay, service level agreement violation ratio, resource utilization, and acceptance ratio. We compare our proposed algorithm with four existing approaches from the literature. Simulation results show that our proposed approach outperforms existing approaches in terms of end-to-end delay, service level agreement violation ratio, resource utilization, and acceptance ratio. [ABSTRACT FROM AUTHOR]

As 5th Generation (5G) and Beyond 5G (B5G) networks become increasingly prevalent, ensuring not only network security but also the security and reliability of the applications, the so-called network applications, becomes of paramount importance. This paper introduces a novel integrated model architecture, combining a network application validation framework with an AI-driven reactive system to enhance security in real-time. The proposed model leverages machine learning (ML) and artificial intelligence (AI) to dynamically monitor and respond to security threats, effectively mitigating potential risks before they impact the network infrastructure. This dual approach not only validates the functionality and performance of network applications before their real deployment but also enhances the network's ability to adapt and respond to threats as they arise. The implementation of this model, in the shape of an architecture deployed in two distinct sites, demonstrates its practical viability and effectiveness. Integrating application validation with proactive threat detection and response, the proposed model addresses critical security challenges unique to 5G infrastructures. This paper details the model, architecture's design, implementation, and evaluation of this solution, illustrating its potential to improve network security management in 5G environments significantly. Our findings highlight the architecture's capability to ensure both the operational integrity of network applications and the security of the underlying infrastructure, presenting a significant advancement in network security. [ABSTRACT FROM AUTHOR]

Network function virtualization allows the implementation of network services on readily available commercial off-the-shelf servers. A primary obstacle is efficiently, autonomously, and dynamically allocating physical resources to virtual network functions that experience fluctuating resource demands. Many existing optimization approaches typically assume static resource demands for each virtual network function instance. Nonetheless, this may result in either resource waste or compromised service quality when an excessive or insufficient allocation of resources occurs. Effectively overseeing virtual network functions presents a considerable challenge due to their dynamic nature. The allocation of resources should be adjusted to align with fluctuations in incoming network traffic. These variations cause significant delays in resource reallocation. Therefore, employing resource estimation models prior to allocation can preemptively address potential issues, fostering dynamic performance enhancements in resource allocation methods. The proposed work utilizes several ensemble machine learning techniques, such as adaboost, bagging, extratrees, histgradient boosting, and lightGBM, and ML models such as bayesian, lasso and poisson regression models, for accurately predicting resource requirements and analyzing performance using CPU resources and throughput. A matching theory-based virtual network function placement algorithm is used for resource allocation. The simulation results show that the acceptance ratio of the ensemble machine learning models integrated with the matching theory-based placement approach is four times higher than that of the fixed resource allocation model. Also, the average delay of the fixed model is five times higher than that of the proposed resource prediction and allocation model. Simulation results also show that the performance of the ensemble machine learning models is better when compared to standard ML models in terms of SFC acceptance ratio and total delay. [ABSTRACT FROM AUTHOR]

In Ultrareliable and Low Latency Communications (URLLC), balancing trade-offs between energy consumption, service availability, and strict reliability and latency requirements is a significant challenge, especially in unmanned aerial vehicle (UAV)-enabled multi-access edge computing (MEC) environments. The constraints imposed by the size, weight and power limitations of UAVs further complicate this task. This study addresses optimizing resource allocation in such environments to meet URLLC demands while minimizing power consumption and maximizing service availability. We explore the virtualization layer of the network function virtualization (NFV)-MEC architecture, incorporating node availability and power consumption alongside conflicting URLLC reliability and latency demands. We introduce an energy-aware model based on continuous-time Markov chain (CTMC) with an embedded virtual resource scaling scheme for Dynamic Resource Allocation (DRA). To solve the optimization problem related to MEC-enabled UAV node dimensioning, we propose a genetic algorithm (GA)-based solution. Our results demonstrate that the proposed GA-based approach achieves a superior balance, with up to a 44% reduction in power consumption compared to the first fit with maximum resources strategy, while also improving service availability and meeting URLLC requirements. This work provides a comprehensive analysis of key virtualization parameters and their impact on critical services within a single NFV-MEC over a UAV node, offering a robust framework for future 6 G network applications. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT), namely, the set of intelligent devices equipped with sensors and actuators and capable of connecting to the Internet, has now become an integral part of the most competitive industries, as it enables optimization of production processes and reduction in operating costs and maintenance time, together with improving the quality of products and services. More specifically, the term Industrial Internet of Things (IIoT) identifies the system which consists of advanced Internet-connected equipment and analytics platforms specialized for industrial activities, where IIoT devices range from small environmental sensors to complex industrial robots. This paper presents an integrated high-level SDN-NFV architecture enabling clusters of smart devices to interconnect and manage the exchange of data with distributed control processes and databases. In particular, it is focused on 5G RAN-MEC slice management in the IIoT context. The proposed system is emulated by means of two distinct real-time frameworks, demonstrating improvements in connectivity, energy efficiency, end-to-end latency and throughput. In addition, its scalability, modularity and flexibility are assessed, making this framework suitable to test advanced and more applications. [ABSTRACT FROM AUTHOR]

Network function virtualization (NFV) allows the dynamic configuration of virtualized network functions to adapt services to complex and real-time network environments to improve network performance. The dynamic nature of physical networks creates significant challenges for virtual network function (VNF) migration and energy consumption, especially in edge–cloud continuum networks. This challenge can be addressed by predicting network traffic and proactively migrating VNFs using the predicted values. However, historical network traffic data are held by network service providers, and different network service providers are reluctant to share historical data due to privacy concerns; in addition, network resource providers that own the underlying networks are unable to effectively predict network traffic. To address this challenge, we apply a federated learning (FL) framework to enable network resource providers to no longer need historical network traffic data to be able to effectively predict network traffic. Further, to enable the predicted network traffic to lead to better migration effects, such as reducing the number of migrations, decreasing energy consumption, and increasing the request acceptance rate, we apply the predicted values of the network traffic to the network environment and feed the migration results of the network environment on the multiple factors described above to the neural network model. To obtain the migration results of the network environment, we analyzed and developed mathematical models for edge–cloud continuum networks with multiple network service providers. The effectiveness of our algorithm is evaluated through extensive simulations, and the results show a significant reduction in the number of migrated nodes and energy consumption, as well as an increase in the acceptance rate of the service function chain (SFC), compared with the commonly used scheme that uses only the difference between the predicted and actual traffic to define the loss function. [ABSTRACT FROM AUTHOR]

Cyberattacks have become more frequent and more violent in recent years. To date, defensive infrastructure has been relatively static, and security functions are usually placed in a common order that does not depend on the current situation. We propose the concept of attack-aware Security Service Function Chain reordering. The idea is to change the order of security functions depending on the malicious traffic observed. We present the basic idea, evaluate the impact of the function chain order, and introduce a framework for function chain reordering. Our evaluation shows that the order often has a significant impact on the performance of the security function chain and that there is no single order that outperforms all other orders in every situation. The proposed proof-of-concept framework successfully validates the feasibility of attack-aware security function chain reordering, and we propose additional extensions to eliminate the remaining deficiencies. [ABSTRACT FROM AUTHOR]

Aiming at the rapid growth of network services, which leads to the problems of long service request processing time and high deployment cost in the deployment of network function virtualization service function chain (SFC) under 5G networks, this paper proposes a multi-agent deep deterministic policy gradient optimization algorithm for SFC deployment (MADDPG-SD). Initially, an optimization model is devised to enhance the request acceptance rate, minimizing the latency and deploying the cost SFC is constructed for the network resource-constrained case. Subsequently, we model the dynamic problem as a Markov decision process (MDP), facilitating adaptation to the evolving states of network resources. Finally, by allocating SFCs to different agents and adopting a collaborative deployment strategy, each agent aims to maximize the request acceptance rate or minimize latency and costs. These agents learn strategies from historical data of virtual network functions in SFCs to guide server node selection, and achieve approximately optimal SFC deployment strategies through a cooperative framework of centralized training and distributed execution. Experimental simulation results indicate that the proposed method, while simultaneously meeting performance requirements and resource capacity constraints, has effectively increased the acceptance rate of requests compared to the comparative algorithms, reducing the end-to-end latency by 4.942% and the deployment cost by 8.045%. [ABSTRACT FROM AUTHOR]

Abstract The air traffic information network is fundamental for ensuring the efficient and safe operation of flights. However, with the increasing demands of air traffic services, the rigidity of the traditional air traffic information network has become increasingly pronounced. Network function virtualization (NFV) technology presents an effective solution to address this issue. Within the NFV environment, the deployment of service function chains (SFC) forms the basis for achieving end-to-end air traffic services. This paper addresses the low delay and high security requirements inherent in air traffic services and proposes an efficient and secure SFC deployment for delay optimization (ESSFCD-DO) method. Primarily, the paper conducts a theoretical analysis of SFC deployment, and derives four key principles related to SFC delay, security, and resource cost. Subsequently, based on these principles, the ESSFCD-DO method is developed, which encompasses the virtual network function (VNF) aggregation algorithm for security and resources cost optimization (VNFAA-SRCO), the VNF deployment algorithm based on topology aware (VNFD-TA), and the algorithm for deploying virtual links based on k-shortest path. Experimental results demonstrate that, in comparison to other SFC deployment methods within this domain, ESSFCD-DO achieves significant optimizations in terms of end-to-end delay of SFC and long-term revenue-cost ratio, while ensuring security and service request acceptance rate.

The advent of Network Function Virtualization (NFV) and Service Function Chains (SFCs) unleashes the power of dynamic creation of network services using Virtual Network Functions (VNFs). This is of great interest to network operators since poor service quality and resource wastage can potentially hurt their revenue in the long term. However, the study shows with a set of test-bed experiments that packet loss at certain positions (i.e., different VNFs) in an SFC can cause various degrees of resource wastage and performance degradation because of repeated upstream processing and transmission of retransmitted packets.To overcome this challenge, this study focuses on resource scheduling and deployment of SFCs while considering packet loss positions. This study developed a novel SFC packet dropping cost model and formulated an SFC scheduling problem that aims to minimize overall packet dropping cost as a Mixed-Integer Linear Programming (MILP) and proved that it is NP-hard. In this study, Palos is proposed as an efficient scheme in exploiting the functional characteristics of VNFs and their positions in SFCs for scheduling resources and deployment to optimize packet dropping cost. Extensive experiment results show that Palos can achieve up to 42.73% improvement on packet dropping cost and up to 33.03% reduction on average SFC latency when compared with two other state-of-the-art schemes.

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Zero-touch networks (ZTNs) can provide autonomous network solutions by integrating software-based solutions for various emerging 5G and 6G applications. The current literature does not provide any suitable end-to-end network management and resource-slicing solutions for service function chaining (SFC) and user intent-based (time and cost preference) 6G/non-6G application execution over ZTNs enabled by mobile edge computing, network function virtualization, and software-defined networking. To tackle these challenges, this work initiates an end-to-end network management and user intent-aware intelligent network resource-slicing scheme for SFC-based 6G/non-6G application execution over ZTNs, taking into account various virtual and physical resources, task workloads, service requirements, and task numbers. The results depicted that at least 25.27% average task implementation delay gain, 6.15% energy gain, and 11.52% service monetary gain are realized in the proposed scheme over the compared schemes. [ABSTRACT FROM AUTHOR]

Network function virtualization (NFV) is a contemporary network architecture concept that integrates complex network functions into software-based virtual network functions (VNFs) instead of specialized hardware. Resource allocation for multiple service function chain requests (SFC) in NFV-enabled networks is a fundamental challenge for network providers. Several research studies have focused on this problem in detail. Specifically, SFC routing attempts to satisfy each request by assigning a path across data centers that places the ordered set of required VNFs. This paper formulates this problem as an SFC provisioning problem (SFCRP) with the two objectives of maximizing the number of accepted requests and balancing the utilization of network resources. To address this problem, a two-stage routing algorithm (2PRT) that integrates Yen's algorithm and a genetic algorithm is proposed. The simulation results based on 12 scenarios that use three real-world networks and randomly generated requests indicate that the batch mode significantly enhances the acceptance rate compared to the existing methods. [ABSTRACT FROM AUTHOR]

With the improvement of service delay and quality requirements for new applications such as unmanned driving, internet of vehicles, and virtual reality, the deployment of network services is gradually moving from the cloud to the edge. This transition has led to the emergence of multi-access edge computing (MEC) architectures such as distributed micro data center and fog computing. In the MEC environment, network infrastructure is distributed around users, allowing them to access the network nearby and move between different service coverage locations. However, the high mobility of users can significantly affect service orchestration and quality, and even cause service interruption. How to respond to user mobility, dynamically migrate user services, and provide users with a continuous and seamless service experience has become a huge challenge. This paper studies the dynamic migration of service function chain (SFC) caused by user mobility in MEC environments. First, we model the SFC dynamic migration problem in mobile scenarios as an integer programming problem with the goal of optimizing service delay, migration success rate, and migration time. Based on the above model, we propose a deep reinforcement learning-driven SFC adaptive dynamic migration optimization algorithm (DRL-ADMO). DRL-ADMO can perceive the underlying network resources and SFC migration requests, intelligently decide on the migration paths of multiple network functions, and adaptively allocate bandwidth, achieving parallel and seamless SFC migration. Performance evaluation results show that compared with existing algorithms, the proposed algorithm can optimize 7% service delay and 20% migration success rate at the cost of sacrificing a small amount of migration time. [ABSTRACT FROM AUTHOR]

With the continuous development of the civil aviation industry toward digitalization and intelligence, the closed architecture of traditional air traffic information networks struggles to meet the rapidly growing demands for air traffic services. Network function virtualization (NFV) is one of the key technologies that can address the rigidity of traditional air traffic information networks. NFV technology has facilitated the flexible deployment of air traffic services, but it has also expanded the attack surface of the network. In addressing the network attack risks faced by service function chains (SFCs) in NFV environments, a SFC protection method based on honeypots and backup technology (PBHB) is proposed to reduce the resource cost of protecting air traffic information networks while enhancing network security. Initially, PBHB utilizes the TAPD algorithm to deploy the primary VNFs as closely as possible to the shortest path between the source and destination endpoints, thus aiming to reduce SFC latency and save bandwidth resource costs. Subsequently, the RAHDR algorithm is employed to install honeypot VNFs in each physical platform that is at risk of side-channel attacks, thus updating the deployment status of honeypot VNFs in real time based on the VNF lifecycle in order to offer primary protection for SFCs. Lastly, the BDMPE algorithm was used to calculate the backup scheme with the highest protection efficiency to implement secondary protection for the SFCs that still do not meet the security requirements. Through experiments, the maximum backup limit for SFCs in PBHB was determined, confirming its satisfactory performance across various SFC arrival rates. Furthermore, performance comparisons with other SFC protection methods revealed that PBHB achieves optimizations in resources cost while ensuring SFC security and latency. [ABSTRACT FROM AUTHOR]

With the fast growth of the Industrial Internet of Everything (IIoE), computing and telecommunication industries all over the world are moving rapidly towards the IPv6 address architecture, which supports virtualization architectures such as Network Function Virtualization (NFV). NFV provides networking services like routing, security, storage, etc., through software-based virtual machines. As a result, NFV reduces equipment costs. Due to the increase in applications on Industrial Internet of Things (IoT)-based networks, security threats have also increased. The communication links between people and people or from one machine to another machine are insecure. Usually, critical data are exchanged over the IoE, so authentication and confidentiality are significant concerns. Asymmetric key cryptosystems increase computation and communication overheads. This paper proposes a lightweight and certificateless end-to-end secure communication scheme to provide security services against replay attacks, man-in-the-middle (MITM) attacks, and impersonation attacks with low computation and communication overheads. The system is implemented on Linux-based Lubuntu 20.04 virtual machines using Java programming connected to NFV-based large-scale hybrid IPv4-IPv6 virtual networks. Finally, we compare the performance of our proposed security scheme with existing schemes based on the computation and communication costs. In addition, we measure and analyze the performance of our proposed secure communication scheme over NFV-based virtualized networks with regard to several parameters like end-to-end delay and packet loss. The results of our comparison with existing security schemes show that our proposed security scheme reduces the computation cost by 38.87% and the communication cost by 26.08%. [ABSTRACT FROM AUTHOR]

Offshore wind farms are growing in complexity and size, expanding deeper into maritime environments to capture stronger and steadier wind energy. Like other domains in the energy sector, the wind energy domain is continuing to digitalize its systems by embracing Industry 4.0 technologies such as the Industrial Internet of Things (IIoT), virtualization, and edge computing to monitor and manage its critical infrastructure remotely. Adopting these technologies creates dynamic, scalable, and cost-effective data-acquisition systems. At the heart of these data-acquisition systems is a communication network that facilitates data transfer between communicating nodes. Given the challenges of configuring, managing, and troubleshooting large-scale communication networks, this review paper explores the adoption of the state-of-the-art software-defined networking (SDN) and network function virtualization (NFV) technologies in the design of next-generation offshore wind farm IIoT–Edge communication networks. While SDN and NFV technologies present a promising solution to address the challenges of these large-scale communication networks, this paper discusses the SDN/NFV-related performance, security, reliability, and scalability concerns, highlighting current mitigation strategies. Building on these mitigation strategies, the concept of resilience (that is, the ability to recover from component failures, attacks, and service interruptions) is given special attention. The paper highlights the self-X (self-configuring, self-healing, and self-optimizing) approaches that build resilience in the software-defined IIoT–Edge communication network architectures. These resilience approaches enable the network to autonomously adjust its configuration, self-repair during stochastic failures, and optimize performance in response to changing conditions. The paper concludes that resilient software-defined IIoT–Edge communication networks will play a big role in guaranteeing seamless next-generation offshore wind farm operations by facilitating critical, latency-sensitive data transfers. [ABSTRACT FROM AUTHOR]

In meeting the diverse and occasionally conflicting quality of service (QoS) requirements associated with modern communication networks, 5G technology has emerged as a pivotal player. In its architecture, 5G has adopted network function virtualization (NFV) and cloud-based approaches, aiming to simplify network and service deployment, operational processes, and management. The convergence of software defined networking (SDN) and NFV offers an effective solution, enabling scalable and high-performance 5G networks. However, this integration poses critical challenges, with the placement of SDN controllers being a central concern due to its significant impact on network performance, covering aspects such as latency, costs, and energy efficiency. This challenge is known as the controller placement problem (CPP). The central theme of this paper revolves around the intricate relationship between 5G core networks, virtualization technology, and the pressing concern of SDN controller placement, underscoring its significance in the modern networking landscape. We provide a survey of recent methodologies aimed at solving the CPP within the realm of SDN, with a particular focus on resiliency and high availability. [ABSTRACT FROM AUTHOR]

Abstract Network function virtualization (NFV) technology deploys network functions as software functions on a generalised hardware platform and provides customised network services in the form of service function chain (SFC), which improves the flexibility and scalability of network services and reduces network service costs. However, irrational resource allocation during service function chain mapping will cause problems such as low resource utilisation, long service request processing time and low mapping rate. To address the unreasonable problem of service mapping resource allocation, an improved service function chain mapping resource allocation method (SA3C) based on the Asynchronous advantageous action evaluation algorithm (A3C) is proposed. This study proposes an SFC mapping model and a mathematical model for joint allocation, which modeled the minimization of processing time as a Markov process. The main network was trained and multiple sub‐networks were generated in parallel using the ternary and deep reinforcement learning algorithm A3C, with the goal of identifying the optimal resource allocation strategy. The experimental simulation results show that compared with the Actor‐Critic (AC) and Policy Gradient (PG) methods, SA3C algorithm can improve the resource utilisation by 9.85%, reduce the total processing time by 10.72%, and improve the mapping rate by 6.72%, by reasonably allocating node computational resources and link bandwidth communication resources.

Summary: P4 combines the benefits of hardware‐based networking with the adaptability of software‐based network operations. However, when faced with intricate network functions, P4 switches reveal constraints in memory and processing primitives. To address these, we advocate offloading traffic demanding intricate processing from the programmable data plane to network function virtualization (NFV). By leveraging this approach, P4 switches handle the core data plane, ensuring maximum performance, whereas virtualized network functions (VNF) cater to the intricate processing. Central to our research is the optimization of this offloading process, specifically considering delay constraints. We developed an analytical model that examines a P4 switch overseen by an SDN controller, integrating an offloading capability to NFV. The principal objective was to determine an offloading rate that minimizes packet processing delay. To this end, we employed a Bounded method, an advancement from Brent's method, to determine this optimal rate. The findings indicate that offloading approximately 66% of packets to the VNF achieves the lowest total delay, registering at 0.1505 μs. This strategy of optimal offloading can notably reduce the system's average delay as the demand for network functions increases. The optimization technique we adopted exhibited rapid convergence in our experiments, reflecting the method's efficacy. Furthermore, a rigorous parametric sensitivity analysis spanning no offloading, full offloading, and optimal offloading strategies underscores that optimal offloading to NFV consistently augments system performance, particularly in terms of delay reduction. Conclusively, our study furnishes valuable insights into offloading strategies, augmenting the narrative on resource allocation in both PNFs and VNFs. [ABSTRACT FROM AUTHOR]

Network function virtualization (NFV) technology deploys network functions as software functions on a generalised hardware platform and provides customised network services in the form of service function chain (SFC), which improves the flexibility and scalability of network services and reduces network service costs. However, irrational resource allocation during service function chain mapping will cause problems such as low resource utilisation, long service request processing time and low mapping rate. To address the unreasonable problem of service mapping resource allocation, an improved service function chain mapping resource allocation method (SA3C) based on the Asynchronous advantageous action evaluation algorithm (A3C) is proposed. This study proposes an SFC mapping model and a mathematical model for joint allocation, which modeled the minimization of processing time as a Markov process. The main network was trained and multiple sub‐networks were generated in parallel using the ternary and deep reinforcement learning algorithm A3C, with the goal of identifying the optimal resource allocation strategy. The experimental simulation results show that compared with the Actor‐Critic (AC) and Policy Gradient (PG) methods, SA3C algorithm can improve the resource utilisation by 9.85%, reduce the total processing time by 10.72%, and improve the mapping rate by 6.72%, by reasonably allocating node computational resources and link bandwidth communication resources. [ABSTRACT FROM AUTHOR]

Network function virtualization (NFV) implements network functions as software, which enables flexible, resource‐efficient, and rapid provision of network services. In NFV, network services are realized by the service function chain (SFC), which is a chain of virtual network functions (VNFs) linked in the proper order. Both availability and deployment cost are key concerns for network operators providing network services as SFC. This paper proposes a flexible VNF placement model on a per‐VNF instance basis that minimizes deployment costs while satisfying availability requirements that may be placed on SFC. This paper uses a multidimensional universal generating function (MUGF) method, which is a multistate system analysis method, to compute the availability of a multistate SFC system with multiple VNFs coexisting on a server. The MUGF method calculates the performance of the entire SFC by combining the performance of servers as determined by applying a continuous‐time Markov chain. To reduce the time to compute the SFC availability, we introduce operators to be applied to MUGF and develop an availability computing method. In addition, a heuristic algorithm for determining VNF placement targeting the lowest deployment cost possible while meeting availability requirements is presented. Numerical results show that the proposed model obtains VNF placement with lower cost than the conventional model in all examined cases. The proposed model achieves VNF placement at 58.5%–75.0% of the deployment cost of the conventional model for the same SFC availability requirements. [ABSTRACT FROM AUTHOR]

The network function virtualization (NFV) feature lies at the core of modern networking, and it allows on-demand real-time integration of new network functions, which is a great benefit for large-scale infrastructure networks. In contrast to the functional benefits, NFV introduces software complexity and computational overhead through additional abstraction layers. The current article addresses the function validation problem in large-scale infrastructure networks of Internet Service Providers (ISPs) and proposes the utilization of blockchain as a validation technology, as opposed to implementing a custom validation solution. The current work showcases a practical architecture implementation to address the service validation in service provider large-scale networks. The POX-based solution to control software-defined networks (SDN) for NFV is extended to offer additional blockchain capabilities. Thus, a blockchain node is integrated and executed in the POX SDN controller. Transaction experiments are performed between two endpoints located in remote locations on the Internet, and the detailed results are presented to validate the utilization of the blockchain technology used on SDNs' control plane. [ABSTRACT FROM AUTHOR]

The need for adaptivity and scalability in telecommunication systems has led to the introduction of a software-based approach to networking, in which network functions are virtualized and implemented in software modules, based on network function virtualization (NFV) technologies. The growing demand for low latency, efficiency, flexibility and security has placed some limitations on the adoption of these technologies, due to some problems of traditional virtualization solutions. However, the introduction of lightweight virtualization approaches is paving the way for new and better infrastructures for implementing network functions. This article discusses these new virtualization solutions and shows a proposal, based on serverless computing, that uses them to implement container-based virtualized network functions for the delivery of advanced Internet of Things (IoT) services. It includes open source software components to implement both the virtualization layer, implemented through Firecracker, and the runtime environment, based on Kata containers. A set of experiments shows that the proposed approach is fast, in order to boost new network functions, and more efficient than some baseline solutions, with minimal resource footprint. Therefore, it is an excellent candidate to implement NFV functions in the edge deployment of serverless services for the IoT. [ABSTRACT FROM AUTHOR]

Zero-touch networks (ZTNs) can provide autonomous network solutions by integrating software-based solutions for various emerging 5G and 6G applications. The current literature does not provide any suitable end-to-end network management and resource-slicing solutions for service function chaining (SFC) and user intent–based (time and cost preference) 6G/non-6G application execution over ZTNs enabled by mobile edge computing, network function virtualization, and software-defined networking. To tackle these challenges, this work initiates an end-to-end network management and user intent–aware intelligent network resource–slicing scheme for SFC-based 6G/non-6G application execution over ZTNs, taking into account various virtual and physical resources, task workloads, service requirements, and task numbers. The results depicted that at least 25.27% average task implementation delay gain, 6.15% energy gain, and 11.52% service monetary gain are realized in the proposed scheme over the compared schemes.

Traditional network service frameworks face significant challenges to accommodate the exponential growth in user traffic. Instead of leveraging proprietary, monolithic hardware appliances, and network functions that power up-to-day telecommunications, Virtualization will be the rightful enabling technology. Virtualization will make it possible for significant cost savings, increased flexibility and scalability in delivering new network services, and network slicing to accommodate widely differing network requirements. An ordered link of virtual network functions (VNFs) for processing traffic flows and providing end-to-end network services in response to a request is called a service function chain (SFC). To address cost reduction and performance enhancement, the primary focus should be on how to deploy VNF instances in substrate network and form SFC with the least amount of latency possible. Intelligent service function placement based on the probability of occurrence of service functions in the service function chain is incorporated in the proposed work. The service function chain is formed dynamically using the required service function. The proposed algorithm uses Dijkstra’s algorithm to find out the best service function node and shortest path between the current service function and previous one. This mechanism will minimize the number of failures in forming a service function chain, latency, and time required to form an SFC.

Quantum computing is commonly considered one highly efficient computing method with the potential to revolutionize computation technology and solve problems that are currently unsolvable. However, due to the limitation of hardware equipment and an immature experimental base, quantum technology is still in its early stages and is far from achieving the expected performance, especially in solving large-scale complex problems. To break through these barriers, we propose a parallelized quantum annealing algorithm based on Lagrangian relaxation. The proposed algorithm divides the large-scale problem into several small problems and then employs multiple quantum computers to solve them. Our proposed approach overcomes the limited number of qubits and allows quantum computing to solve largescale optimization problems. Additionally, we incorporate a local search method to ensure this Lagrangian relaxation based quantum algorithm achieves an optimal solution. We use the proposed parallelized quantum annealing algorithm to solve optimal scheduling problems in network function virtualization networks. The problem is expressed in a linear optimization model that is NP-hard. Our proposed algorithm presents excellent time performance in solving this virtualized network functions scheduling problem, compared with the Lagrangian relaxation based classical algorithm.

Telco edge data centers (DCs) accommodate applications whose load fluctuates considerably during a day. This variability mandates swift and responsive resource adjustments to mitigate the risk of incurring unnecessary costs during off-peak periods, where a significant fraction of nodes may be under-utilized. Tackling this challenge is integral to optimizing operational efficiency and cost-effectiveness of telco edge DCs. To this end, this article aims to address the Dynamic Data Center Sizing (DDS) problem, which boils down to optimizing the number of active nodes as per current resource demand. The proposed DDS solution consists of two core modules, namely a forecasting module, which predicts resource demands, and a decision-making module, which acts upon predicted demands. The decision-making of DDS is implemented via the filtering and the rank-drain-observe (RDO) algorithms. Filtering is based on integer linear programming, and it computes the theoretically optimal state of the DC based on predicted resource demands. RDO is a heuristic that strives to realize the optimal DC state in an iterative and robust fashion. To expedite RDO in large-scale clusters, we further devise a Reinforcement Learning (RL)-enabled DDS variant (i.e., RL-DDS), in which RDO integrates an RL agent that computes optimized batches of nodes for concurrent deactivation. We propose an innovative solution based on the notion of Digital Twin to train the RL agent in emulation mode. DDS and RL-DDS are evaluated upon real-life testbeds resembling actual DCs. Results demonstrate significant cost reduction, ranging from 7% in conservative and relatively static scenarios, up to 38% in highly dynamic settings.

In the context of service provisioning, the integration of Network Functions Virtualization (NFV) enhances the flexibility, scalability, and programmability of telecommunication networks. However, this integration introduces challenges, particularly in optimizing the placement of Virtualized Network Functions (VNFs) within the NFV Infrastructure (NFVI). Existing studies have predominantly focused on well-connected, mains-powered ecosystems like datacentres and cloud networks. In contrast, the aim of this paper is to identify a solution that distributes and deploys a Wireless Mesh Network (WMN) as the backbone for a disaster management communication and service infrastructure. Given the mobility of mesh routers in such scenarios, these devices are often battery-powered. Consequently, the placement of VNFs directly impacts the energy consumption in the network and, subsequently, its lifetime. The proposed solution for the energy-efficient placement of VNF is formulated as a multi-objective optimization problem. This context introduces different approaches and proposes a heuristic algorithm to optimize the placement of VNFs. The evaluation results indicate that the proposed algorithm outperforms prior alternatives in various scenarios. Notably, it surpasses established methods like the Nondominated Sorting Genetic Algorithm II (NSGA-II), commonly used to solve similar problems. This research signifies a significant advancement in addressing the specific challenges associated with NFV integration in wireless mesh networks, particularly in disaster management contexts.

In the 5G and beyond 5G networks, achieving security-aware data transmission needs to convert clients ’ requests into a service function chain (SFC), each service function (SF) providing a certain security guarantee. With diverse configuration techniques, an SF may own multiple versions, each version providing various security guarantees with diverse costs. It should be notice that, as the recent software failures have caused severe financial loss, great attentions from both academia and industry have been put onto the SFC reliability. In the literature, existing works have solely investigated the following two fields: 1) how to deploy a security-aware SFC, and 2) how to protect a traditional SFC. Simply applying these techniques to dealing with the problem of security-aware SFC protection might not be efficient as the backup and primary SFCs may not be identical for security-aware SFCs. Therefore, how to jointly take these fields into account is challenging and remains open. To tackle the above problem, this paper studies how to construct and embed a security-aware SFC with asymmetric dedicated protection. We mathematically define this problem and name it security-aware service function chaining, embedding, and protection with multi-versioned SFs (SFCEP-MF) with the objective of cost optimization. Next, to optimize the SFCEP-MF problem, we construct an efficient algorithm, called augmenting-path with primary-first disjoint SFP identifier (APPF-DSI). Extensive simulation results show that the APPF-DSI algorithm outperforms the benchmark approaches that are directly extended from the state-of-the-art.

Network slicing has emerged as a cornerstone technology within the 5G ecosystem, enabling efficient resource allocation, service customization, and support for various applications. Its ability to deliver Network-as-a-Service (NaaS) brings a new paradigm of adaptable and efficient network provisioning. However, with the diversification of services and the increasing complexity of network infrastructures, a simultaneous rise in security vulnerabilities becomes evident. These flaws go beyond the limitations of conventional network security and affect various aspects of network slice (NS) implementation and management. The limitations of traditional security, such as static policies, single point of failure, and challenges in effectively securing network slicing deployments, underscore the need to explore security measures tailored to the dynamic nature of 5G networks. To ensure the robust security of 5G networks, it is essential to consider various security concerns such as isolation, authentication, and authorization. Furthermore, dynamic orchestration and inter-slice communication security challenges must be proactively tackled. The security concerns related to 5G networks must be addressed comprehensively to ensure the safe and secure operation of the network. Our survey paper goes into these complex security issues, providing an in-depth and systematic review of the various contexts in which they emerge. We have identified the five most vulnerable areas in Network Slicing: Slice-Lifecycle, Communication type slice uses, Technologies used to provide service, Management threats, and End Devices utilized in service. Apart from threats in these vulnerable areas, we also discussed a few generous attacks that can be launched to disrupt network-slicing services. Furthermore, this study is a valuable resource for evaluating the current state of research efforts in this domain, contributing to the ongoing enhancement of security measures and the overall robustness of network-slicing technology. In doing so, we aim to ensure the secure and sustainable evolution of 5G networks as they become increasingly integral to our digital infrastructure.

Network Function Virtualization (NFV) empowers Internet Service Providers (ISPs) to place Virtual Network Functions (VNFs) efficiently in order to enhance the network performance without incurring a high cost. In this environment, Service Function Chains (SFCs) always need to steer the traffic through a sequence of VNF instances. Therefore, ISPs must adopt a suitable SFC embedding strategy to bolster their revenue. However, existing VNF placement and chaining methodologies harbour unrealistic assumptions, as they tackle the mapping problem from a generic standpoint, overlooking the distinctive characteristics of the constituent VNFs within a chain. Hence, they are not efficient when the strict requirements of life-critical applications, such as telesurgery, need to be satisfied. In this paper, taking into account the strict requirements of telesurgery-like systems, we present a cost-efficient characteristic-aware SFC mapping method for telesurgery Systems in the Cloud-Edge continuum. We formulate this problem as a Binary Linear Programming (BLP) model to embed SFC requests at minimal cost. Also, we propose an innovative heuristic algorithm that allocates each VNF based on its distinctive characteristics. Simulation results demonstrate that taking the characteristics of the VNFs into account when addressing the placement problem improves the system performance notably.

In the midst of rising global population and environmental challenges, smart agriculture emerges as a vital solution by integrating advanced technologies to optimize agricultural practices. Through data-driven insights and automation, it tackles the necessity for sustainable resource management, enhancing productivity and resilience in the face of complex food security and ecological concerns. The prospects of utilizing the Internet of Things (IoT) for smart agriculture are tremendous, where many IoT devices can be deployed for local environment monitoring, precision farming, autonomous irrigation, and, soil management. In some use cases like smart monitoring and agrochemical applications, UAV-enabled mobile-edge computing (MEC) is proposed as an enabler to provide IoT nodes with additional resources by hosting their computation functions. From the implementation perspective, to flexibly manage the computation functions in UAVs and/or MEC server, the emerging network function virtualization (NFV) can be utilized. However, efficient orchestration of the virtualized functions would be a challenge. In this paper, we consider a decentralized UAV-aided MEC system for smart agricultural applications in which the processing nodes benefit from the NFV technology. We aim to propose a method for efficiently orchestrating the NFVs while some important metrics are minimized, i.e., the age of information (AoI) and total network energy consumption. Especially, we consider the case in which the network state is not fully observable to the orchestrator or the observations are exposed to uncertainties. Consequently, the problem is formulated as a decentralized partially observable Markov decision process (DEC-POMDP). As the formulated problem is NP-complete, we exploit some structural features of the proposed scheme to introduce the concept of symmetry and simplify the problem. Then, a novel decentralized federated learning-based solution is proposed to solve the problem. Simulation results show the effectiveness of the proposed approach in minimizing the total network energy consumption and achieving $AoI$ values less than $200\:msec$ to support demanding real-time applications.

Future network architectures are expected to be autonomous, intelligent, and service-based, posing new security challenges. To address these challenges, the Artificial Intelligence (AI) security service emerges as a promising solution. However, the complex service configurations and performance guarantees hinder the autonomous deployment of the AI security service. This paper proposes an autonomous deployment mechanism in Software-Defined Networking/Network Function Virtualization (SDN/NFV) enabled networks. First, our mechanism introduces user and decision planes on top of the control plane, enabling hierarchical intent expression and translation from user security intent to security policies. Then, we analyze the embedding problem of the AI-based Security Function Chain (AISFC) during security policy generation. We formulate the AISFC embedding problem as an Integer Linear Programming (ILP) task to minimize the total response delay. By decomposing it into AISF placement and routing, we design a heuristic algorithm with polynomial time complexity. Finally, we validate the proposed mechanism through a prototype system and numerical simulations, demonstrating its ability to autonomously translate, implement, and guarantee the user security intent. Comparative analysis shows that our approach considering the relationship between available computing resources and delay achieves smaller response delays than the baseline. Furthermore, our algorithm achieves a gap from optimality approximately 28.57% smaller than the greedy algorithm and supports networks that are 4.34 times larger in scale than the exact solution within a 2-second execution time.

Software Defined Networking (SDN) and Network Function Virtualization (NFV) are two transformative technologies that offer distinct benefits. SDN virtualizes the control plane by separating it from the data plane, while NFV virtualizes the data plane by moving network functions from hardware and implementing them in software. Therefore, combining SDN and NFV can fully exploit the benefits of both technologies. As Programming Protocol-independent Packet Processors (P4) become popular due to its flexibility, traditional SDN switches are being replaced by P4 switches. In the P4+NFV architecture, network functions can be provided in both P4 switches (PNF) and NFV servers (VNF). However, to minimize packet delay, the offloading problem between P4 switches and NFV needs to be addressed. The novelty of our paper lies in investigating the offloading problem and evaluating the impact of employing multiple VNFs with varying computing capacities within the P4+NFV architecture. We also use M/M/1 queuing theory to derive the average packet delay and propose an optimization solution based on gradient descent to find out the optimal offloading probabilities of various VNF servers. Results show that optimal offloading from P4 switch to multiple VNFs can reduce the average packet delay from 4.76% to 40.02%.

The Internet of Things (IoT), namely, the set of intelligent devices equipped with sensors and actuators and capable of connecting to the Internet, has now become an integral part of the most competitive industries, as it enables optimization of production processes and reduction in operating costs and maintenance time, together with improving the quality of products and services. More specifically, the term Industrial Internet of Things (IIoT) identifies the system which consists of advanced Internet-connected equipment and analytics platforms specialized for industrial activities, where IIoT devices range from small environmental sensors to complex industrial robots. This paper presents an integrated high-level SDN-NFV architecture enabling clusters of smart devices to interconnect and manage the exchange of data with distributed control processes and databases. In particular, it is focused on 5G RAN-MEC slice management in the IIoT context. The proposed system is emulated by means of two distinct real-time frameworks, demonstrating improvements in connectivity, energy efficiency, end-to-end latency and throughput. In addition, its scalability, modularity and flexibility are assessed, making this framework suitable to test advanced and more applications.

With the emergence of various delay-sensitive applications, how to improve the system's delay performance has be come a hot topic in academic. However, it is difficult for existing multi-domain embedding algorithms to meet the delay per- formance requirements of virtual networks. Therefore, to solve this problem, this paper proposed a TDS-VNE algorithm. In the node embedding phase, it defined a propagation delay evaluation function(D). In the link embedding phase, it defined a path delay sensing parameter. Simulation results show that the proposed embedding algorithm reduces the average network propaga tion delay and has good results on the virtual network request acceptance rate and long-term benefit-cost ratio [ABSTRACT FROM AUTHOR]

Software Defined Network (SDN) and Network Function Virtualization (NFV) technology promote several benefits to network operators, including reduced maintenance costs, increased network operational performance, simplified network lifecycle, and policies management. Network vulnerabilities try to modify services provided by Network Function Virtualization MANagement and Orchestration (NFV MANO), and malicious attacks in different scenarios disrupt the NFV Orchestrator (NFVO) and Virtualized InfrastructureManager (VIM) lifecycle management related to network services or individual Virtualized Network Function (VNF). This paper proposes an anomaly detection mechanism that monitors threats in NFV MANO and manages promptly and adaptively to implement and handle security functions in order to enhance the quality of experience for end users. An anomaly detector investigates these identified risks and provides secure network services. It enables virtual network security functions and identifies anomalies in Kubernetes (a cloud-based platform). For training and testing purpose of the proposed approach, an intrusion-containing dataset is used that hold multiple malicious activities like a Smurf, Neptune, Teardrop, Pod, Land, IPsweep, etc., categorized as Probing (Prob), Denial of Service (DoS), User to Root (U2R), and Remote to User (R2L) attacks. An anomaly detector is anticipated with the capabilities of a Machine Learning (ML) technique, making use of supervised learning techniques like Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), and Extreme Gradient Boosting (XGBoost). The proposed framework has been evaluated by deploying the identified ML algorithm on a Jupyter notebook in Kubeflow to simulate Kubernetes for validation purposes. RF classifier has shown better outcomes (99.90% accuracy) than other classifiers in detecting anomalies/intrusions in the containerized environment. [ABSTRACT FROM AUTHOR]

Green Mobile Edge Networks（GMENs） is an emerging distributed network that uses renewable green energy as the main energy source for edge nodes to operate, thereby reducing energy acquisition from the power grid. In GMENs, network service providers can form a service function chain（SFC） of multiple virtual network functions（VNFs） in a specific order to process user requests. These VNFs can flexibly deploy in the form of software instances on edge nodes to efficiently utilize green energy. To ensure the latency and reliability requirements of the service, it is necessary to deploy multiple backup VNFs（BVNF） on nodes within a k-hop range of VNF, to replace it in providing services that meet the latency requirements in the event of VNF failure. However, the green energy collected by each geographically dispersed edge node is dynamically changing. If the collected energy of nodes around the VNF decreases and is less than the energy consumption required to operate the BVNF, the node needs to harvest additional energy from the grid to guarantee its operation. Therefore, developing an online joint deployment plan for VNF and BVNF based on the green energy collected by nodes and the energy rate consumed by computing, communication, and other resources is an NP-hard problem, which minimizes the energy obtained by edge nodes from the power grid while ensuring service latency and reliability requirements. At present, there have been many studies on this issue and certain achievements have been made, but there are still some problems in practical applications. To further promote research in this field, this article analyzes, summarizes, and compares the latest research achievements in recent years, their advantages and disadvantages, and provides prospects for future work. [ABSTRACT FROM AUTHOR]

A typical healthcare system needs further participation with patient monitoring, vital signs sensors and other medical devices. Healthcare moved from a traditional central hospital to scattered patients. Healthcare systems receive help from emerging technology innovations such as fifth generation (5G) communication infrastructure: internet of things (IoT), machine learning (ML), and artificial intelligence (AI). Healthcare providers benefit from IoT capabilities to comfort patients by using smart appliances that improve the healthcare level they receive. These IoT smart healthcare gadgets produce massive data volume. It is crucial to use very high-speed communication networks such as 5G wireless technology with the increased communication bandwidth, data transmission efficiency and reduced communication delay and latency, thus leading to strengthen the precise requirements of healthcare big data utilities. The adaptation of 5G in smart healthcare networks allows increasing number of IoT devices that supplies an augmentation in network performance. This paper reviewed distinctive aspects of internet of medical things (IoMT) and 5G architectures with their future and present sides, which can lead to improve healthcare of patients in the near future. [ABSTRACT FROM AUTHOR]

Software Defined Networking (SDN) and Network Function Virtualization (NFV) are two transformative technologies that offer distinct benefits. SDN virtualizes the control plane by separating it from the data plane, while NFV virtualizes the data plane by moving network functions from hardware and implementing them in software. Therefore, combining SDN and NFV can fully exploit the benefits of both technologies. As Programming Protocol-independent Packet Processors (P4) become popular due to its flexibility, traditional SDN switches are being replaced by P4 switches. In the P4+NFV architecture, network functions can be provided in both P4 switches (PNF) and NFV servers (VNF). However, to minimize packet delay, the offloading problem between P4 switches and NFV needs to be addressed. The novelty of our paper lies in investigating the offloading problem and evaluating the impact of employing multiple VNFs with varying computing capacities within the P4+NFV architecture. We also use M/M/1 queuing theory to derive the average packet delay and propose an optimization solution based on gradient descent to find out the optimal offloading probabilities of various VNF servers. Results show that optimal offloading from P4 switch to multiple VNFs can reduce the average packet delay from 4.76% to 40.02%. [ABSTRACT FROM AUTHOR]

Offshore wind farms are growing in complexity and size, expanding deeper into maritime environments to capture stronger and steadier wind energy. Like other domains in the energy sector, the wind energy domain is continuing to digitalize its systems by embracing Industry 4.0 technologies such as the Industrial Internet of Things (IIoT), virtualization, and edge computing to monitor and manage its critical infrastructure remotely. Adopting these technologies creates dynamic, scalable, and cost-effective data-acquisition systems. At the heart of these data-acquisition systems is a communication network that facilitates data transfer between communicating nodes. Given the challenges of configuring, managing, and troubleshooting large-scale communication networks, this review paper explores the adoption of the state-of-the-art software-defined networking (SDN) and network function virtualization (NFV) technologies in the design of next-generation offshore wind farm IIoT–Edge communication networks. While SDN and NFV technologies present a promising solution to address the challenges of these large-scale communication networks, this paper discusses the SDN/NFV-related performance, security, reliability, and scalability concerns, highlighting current mitigation strategies. Building on these mitigation strategies, the concept of resilience (that is, the ability to recover from component failures, attacks, and service interruptions) is given special attention. The paper highlights the self-X (self-configuring, self-healing, and self-optimizing) approaches that build resilience in the software-defined IIoT–Edge communication network architectures. These resilience approaches enable the network to autonomously adjust its configuration, self-repair during stochastic failures, and optimize performance in response to changing conditions. The paper concludes that resilient software-defined IIoT–Edge communication networks will play a big role in guaranteeing seamless next-generation offshore wind farm operations by facilitating critical, latency-sensitive data transfers.