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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 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]

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.

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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]

Network function virtualization can increase the versatility of computing and network platforms, enabling them to provide more flexible services. In terms of a softwaredefined network and network function virtualization environment, this paper explores the joint virtual network function (VNF) deployment and traffic routing in multisource multicast for VNF service chain deployment to minimize the deployment overhead, transmission delay. Firstly, a multi-objective optimization model, which minimizes the deployment overhead and transmission delay, is established to tackle this challenge problem. Then, a highly efficient coding scheme, crossover, and mutation operators are designed. Based on these, an improved genetic algorithm in multi-objective evolutionary algorithm based on decomposition (MOEA/D) framework (GA-MOEA/D) is proposed to solve this multi-objective problem. Finally, simulation experiments are conducted using two widely used network topologies in order to demonstrate the performance of the proposed algorithm. The simulation results demonstrate that the proposed algorithm can obtain the smaller deployment overhead, transmission delay, load degree and energy consumption 5.2%-15.61%, 11.2%-26.4%, 8.3%-17.6% and 7.8%-14.4% than that of the compared algorithm, respectively. [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]

Network function virtualization technology transforms hardware middleboxes into sets of software-based Virtual Network Function (VNF ) that can host the growing demand for latency-sensitive services at the fog-cloud computing-based networks. Dynamic placement of service functions chain by reusing (VNF) instances can improve resource utilization and save time. To address this problem, we propose a parallelized service function chain placement method by reusing (VNF) in fog-cloud computing-based networks. Here, the placement problem is configured using deep reinforcement learning approaches with the aim of maximizing long-term cumulative reward. By sharing (VNF) s in parallel, this method can achieve computational acceleration in providing online services. In addition, the proposed method increases the ability to accept future requests by extracting the distribution of the initialized (VNF) s. The simulation results show the superiority of the proposed method, where considering the monetary cost criterion of more than 7%, it performs better than the best existing methods.

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]

Low-speed internet can negatively impact incident response by causing delayed detection, ineffective response, poor collaboration, inaccurate analysis, and increased risk. Slow internet speeds can delay the receipt and analysis of data, making it difficult for security teams to access the relevant information and take action, leading to a fragmented and inadequate response. All of these factors can increase the risk of data breaches and other security incidents and their impact on IoT-enabled communication. This study combines virtual network function (VNF) technology with software -defined networking (SDN) called virtual network function software-defined networking (VNFSDN). The adoption of the VNFSDN approach has the potential to enhance network security and efficiency while reducing the risk of cyberattacks. This approach supports IoT devices that can analyze large volumes of data in real time. The proposed VNFSDN can dynamically adapt to changing security requirements and network conditions for IoT devices. VNFSDN uses threat filtration and threat-capturing and decision-driven algorithms to minimize cyber risks for IoT devices and enhance network performance. Additionally, the integrity of IoT devices is safeguarded by addressing the three risk categories of data manipulation, insertion, and deletion. Furthermore, the prioritized delegated proof of stake (PDPoS) consensus variant is integrated with VNFSDN to combat attacks. This variant addresses the scalability issue of blockchain technology by providing a safe and adaptable environment for IoT devices that can quickly be scaled up and down to pull together the changing demands of the organization, allowing IoT devices to efficiently utilize resources. The PDPoS variant provides flexibility to IoT devices to proactively respond to potential security threats, preventing or mitigating the impact of cyberattacks. The proposed VNFSDN dynamically adapts to the changing security requirements and network conditions, improving network resiliency and enabling proactive threat detection. Finally, we compare the proposed VNFSDN to existing state-of-the-art approaches. According to the results, the proposed VNFSDN has a 0.08 ms minimum response time, a 2% packet loss rate, 99.5% network availability, a 99.36% threat detection rate, and a 99.77% detection accuracy with 1% malicious nodes. [ABSTRACT FROM AUTHOR]

Network slicing shows promise as a means to endow 5G networks with flexible and dynamic features. Network function virtualization (NFV) and software-defined networking (SDN) are the key methods for deploying network slicing, which will enable end-to-end (E2E) isolation services permitting each slice to be customized depending on service requirements. The goal of this investigation is to construct network slices through a machine learning algorithm and allocate resources for the newly created slices using dynamic programming in an efficient manner. A substrate network is constructed with a list of key performance indicators (KPIs) like CPU capacity, bandwidth, delay, link capacity, and security level. After that, network slices are produced by employing multi-layer perceptron (MLP) using the adaptive moment estimation (ADAM) optimization algorithm. For each requested service, the network slices are categorized as massive machine-type communications (mMTC), enhanced mobile broadband (eMBB), and ultra-reliable low-latency communications (uRLLC). After network slicing, resources are provided to the services that have been requested. In order to maximize the total user access rate and resource efficiency, Dijkstra's algorithm is adopted for resource allocation that determines the shortest path between nodes in the substrate network. The simulation output shows that the present model allocates optimum slices to the requested services with high resource efficiency and reduced total bandwidth utilization. [ABSTRACT FROM AUTHOR]

Summary: Network function virtualization (NFV) is a name of technology for replacing hardware‐based network functions with software programs. Virtual network function (VNF) is a software program that replaces the hardware‐based network functionality. The replacement of the hardware‐based network functions (middleboxes) with software programs promises the on‐demand provisioning of network functions and reduces capital and operational expenses of the network. Due to this replaced network can adapt to the different network functions. Network service providers deploy various network services with different objectives, such as reducing the network's active servers and traffic latency or network operational expenses. In this article, a VNF placement problem is studied to optimize the total operating costs of the networks. To solve the VNF placement problem, we proposed an integer linear program (ILP) model, which has been implemented using CPLEX. Although an ILP‐based approach gives an optimal solution, it takes a long execution time to find the solution. Due to the long execution time, the ILP‐based approach is not suitable for the real‐time VNF placement problem. To address this challenge, we proposed a heuristic based on dynamic programming that performs better than the existing approaches. The simulation results of the proposed solution using real‐world topologies show that the heuristic approach finds a feasible solution that is only 1 to 1.34 times far from the optimal one. Moreover, experimental results show that the proposed heuristic is 15 to 423 times faster than the ILP. [ABSTRACT FROM AUTHOR]

Modern networks urge agility, flexibility, and capacity to cope with the growing demand for media content and applications increasingly oriented toward data consumption. The Central Offices (CO) of telecommunication providers, being a vital aggregator of different access networks, such as optical and mobile, need to be prepared to deal with these demands. The Open Broadband-Broadband Access Abstraction (OB-BAA) architecture fits into the initiative to modernize the Information Technology (IT) components of broadband networks, more specifically the COs. This paper discusses the development of a Virtualized Network Function (VNF) in the context of network security to be integrated as a component of an OB-BAA architecture guided by the Software-Defined Network paradigm. More specifically, the authentication and authorization of network equipment within the IEEE 802.1X protocol are applied to Next Generation Passive Optical Networks. The VNF development is based on the Golang language combined with gRPC programmable interfaces for communication between the various elements of the OB-BAA architecture, and then the components were “containerized” and inserted in the Docker and Kubernetes virtualization frameworks of a multinational telecommunications operator. Finally, performance metrics such as computational resource usage (CPU, memory, and network I/O) and execution time of VNF processes were analyzed in usage tests with multiple supplicants and distinct operational modes, to attest to the most promising virtualization scenarios. [ABSTRACT FROM AUTHOR]

Network function virtualization (NFV) provides dynamic, energy-efficient, and cost-effective network services by segregating network services from expensive and energy-hungry hardware components. Traditional hardware-based network functions are always ON with 100% capacity, resulting in massive energy consumption. In contrast, NFV enables different network services (NS) through virtual network functions (VNFs), which can reduce energy consumption, by automatic ON/OFF (auto-scaling) of the VNFs depending on the traffic load. However, auto-scaling will introduce delays in the network service management process. This delay can be minimized by estimating future resource requirements accurately and taking proactive steps ahead of time. In this research, we propose the use of deep learning based forecasting algorithms to predict the required VNFs and virtual CPU for each VNF to complete a network service, often deployed as service function chain (SFC). We use long short term memory (LSTM), bidirectional-LSTM, and convolutional neural network (CNN) based algorithms to forecast resources from univariate and multivariate network traffic data. Experimental results show that CNN and bidirectional-LSTM-based forecasting models can forecast more accurately than the prior studies. Based on the forecasting models, we propose a resource allocation algorithm (DeepVRM) that can efficiently allocate resources to complete SFC. [ABSTRACT FROM AUTHOR]

The idea of network function virtualization has emerged recently as a means of accelerating the deployment of middleboxes and network operations while also lowering deployment costs. Service function chaining provides network connectivity and steers traffic between the deployed VNF instances to provide a network service. However, integrating NFV within SFC scheduling introduces complexities, particularly in efficiently allocating resources to VNFs amidst dynamic network traffic and service demands. Optimization of VNF placement and scheduling is essential to minimize execution costs while meeting stringent Service Level Agreements (SLAs) and ensuring quality-guaranteed services. Moreover, the convergence of NFV and SFC scheduling brings for security challenges, including unauthorized access, data interception, and service disruption. Balancing optimization objectives with stringent security requirements poses a non-trivial task, emphasizing the need for prioritizing security in resource allocation and scheduling decisions. Meeting SFC deadlines is challenging due to dynamic network conditions, service demands, and resource allocation complexity. Failure to meet deadlines can lead to service quality degradation, SLA violations, and financial penalties. To address these challenges, the security and cost-aware SFC scheduling problem is formulated as an optimization problem. Moreover, a three-level security model is designed for both the VNFs and the physical machines in the NFV-enabled networks. Since the problem is NP-hard, we propose two heuristics named Particle Swarm Optimization-Based SFC Scheduling approach and Group Learning Particle Swarm Optimization-Based SFC Scheduling model that focus on optimizing the execution cost of SFC while meeting security and deadline requirements. The proposed models are compared with the existing SFC scheduling models. The effectiveness of the proposed scheduling approaches is evaluated through extensive simulations and it is shown that proposed scheduling approaches outperform the existing models in terms of average execution cost, security violation ratio, deadline violation ratio, service level agreement violation ratio, and average delay. [ABSTRACT FROM AUTHOR]

Designing efficient and flexible approaches for placement of Virtual Network Function (VNF) chains is the main success of Network Function Virtualization (NFV). However, most current work considers the constant bandwidth and flow processing requirements while deploying the VNFs in the network. The constant (immutable) flow processing and bandwidth requirements become critical limitations in an NFV-enabled network with highly dynamic traffic flow. Therefore, bandwidth requirements and available resources of the Point-of-Presence (PoP) in the network change constantly. We present an adaptive model for placing VNF chains to overcome this limitation. At the same time, the proposed model minimizes the number of changes (i.e., re-allocation of VNFs) in the network. The experimental evaluation shows that the adaptive model can deliver stable network services. Moreover, it reduces the significant number of changes in the network and ensures flow performance.

The emergence of Network Function Virtualization enables the deployment of network services in the form of service function chains. In this context, one of the key challenges is to ensure the survivability of these chains in face of single or multiple simultaneous physical node failures. In this paper, we address this challenge and propose solutions to guarantee the survivability of service chains by ensuring that there are enough backups ready to take over when failures occur. Specifically, we put forward a Survivability Management Framework that predicts traffic demand in service function chains and provision enough backups for network functions with minimal costs. To this end, we leverage the AutoRegressive Integrated Moving Average (ARIMA) model to predict future demand. We mathematically model the service chain survivability problem as an integer linear program that determines the minimal number of shared backups and their optimal location in the infrastructure such that backup operational costs are minimized. We also devise two greedy algorithms to deal with the problem in large-scale scenarios. We show, through several simulations, the performance and efficiency of the proposed solutions in different scenarios. We also show that demand prediction could help to avoid unnecessary provisioning of backups, and thereby reduce their operational costs. [ABSTRACT FROM AUTHOR]

Fog computing computes and stores data locally based upon distributed architectures that extend cloud to the edge of the network. This enables fog paradigms to be suitable for delay‐sensitive applications that demand various network functions (NFs). These NFs here are provided using network function virtualization (NFV) to allow elastic NF deployment on the fog nodes, that is, virtual network functions (VNFs). Now a major challenge for NFV‐based fog architectures is the limited resources, which vulnerate them to premature saturation and load imbalance. Hence resource management schemes are necessary for fog networks, in particular load migration and redistribution. Along this, an efficient migration scheme is proposed here using the coordinated generalized pattern search (CGPS) heuristic method. The scheme migrates excess load from heavily‐loaded (HL) nodes to lightly‐loaded (LL) nodes in proximity. The scheme adopts two criteria, termed as exclusive and common use of VNF instances. Results show that the proposed scheme achieves high migration success at reduced delays, as well as alleviating resources with low migration frequency. [ABSTRACT FROM AUTHOR]

This paper proposed an SFC (service function chaining） placement algorithm based on VNF (virtual network function） instance sharing to address the problem of difficult tradeoff between the deployment cost of VNF instances and forwarding cost in the process of SFC placement. Firstly, for multi-link SFC, it constructed the VNF and virtual link mapping model, and estimated the upper bound of the path deployment length to ensure the SFC delay. Secondly, within the limit of the path length, it maximized the number of the reused VNF instance as much as possible to balance the forwarding cost and the deployment cost of VNF. Finally, it obtained the SFC placement strategy. Compared with the existing SPH (shortest path heuristic） and GUS (greedy on used server） placement algorithms, the total operating expense of the proposed algorithm is 6.6% and 12.15% lower, and when the number of SFC increases, the service acceptance rate of this algorithm can reach 89.33%.The simulation experimental results show that this algorithm can effectively reduce the SFC placement cost while ensuring the quality of service. [ABSTRACT FROM AUTHOR]

As a new cloud service mode, mobile edge computing distributes the traditional centralized deployment and management of cloud resources to the wireless access network. Mobile services can be processed for the sake of obtaining a good service experience and reduce the network load of the backtrip network. In order to effectively improve the end-to-end service delay of the flow in multi-clusters coexisting mobile edge computing (MEC) network, an improved virtual network function (VNF) service chain deployment algorithm based on Q-learning was proposed. Based on the planning model, a spatial-temporal optimization model of VNF service chain deployment was established by markov decision process, and an improved BPQ-learning algorithm was designed to solve the model. The method considered both the virtual deployment of MEC service chain in space dimension and the life cycle management of VNF in time dimension. The optimization of VNF deployment is realized. The possible network congestion is avoided by deployment service nodes in advance. Experimental results show that the proposed strategy can provide end-to-end services with lower latency and better experience for delaysensitive mobile services under different VNF service chain scale, service node scale, cluster number and logical connection relationship among the virtual network functions. [ABSTRACT FROM AUTHOR]

In order lo meet the fine-grained security isolation requirements of 5 G vertical users for network slicing deployment, while taking into account the isolation requirements of users and improving resource utilization, this paper proposed a network slicing security deployment method based on the improved BN model. Firstly, this paper proposed a dual-layer BN model network slicing deployment architecture, and designed a dual-layer virtualization architecture of virtual machine container based on SBA . It assigned conflict of interest labels to the network slicing according to the isolation requirements of users. It determined the isolation deployment strategy of network slice based on the improved BN model deployment rules. Then, it established the deployment method as an integer linear programming model, and took the deployment cost as the objective function. This model realized the low-cost deployment network slices by minimizing the objective function. Finally, this paper used genetic algorithm to solve the problem. The experimental results show that the proposed security deployment method can reduce the deployment cost on the premise of meeting the requirements of network slice security isolation. [ABSTRACT FROM AUTHOR]

The abstraction of the network node functions using virtualization methods introduced an innovative architecture called Network Function Virtualization (NFV). In NFV, every virtualization software hosts a network service recognized as a Virtual Network Function (VNF). In general, the network provider creates a Service Function Chain (SFC) for every sequence of multiple requested VNFs by the customers. Although NFV allows for a more flexible and economical approach, it is more prone to error and failure. Therefore, providing reliable provisioning for VNF chaining is one of the key issues in NFV. In this paper, we present a systematic literature review to study the pioneer research efforts that provide reliable provisioning for VNF chaining by guaranteeing the availability of the service and resource optimization. Our review is the result of the analysis of 21 screened papers. This paper presents the result of our analysis, including different aspects of a reliable provisioning algorithm, various adopted techniques for reliable provisioning, and the superiority and drawbacks of each algorithm based on the proposed criteria for the evaluation of the provisioning algorithms. [ABSTRACT FROM AUTHOR]

In Network Function virtualization (NFV), network functions are virtualized as Virtual Network functions (VNFs). A network service consists of a set of VNFs. One of the major challenges in implementing this paradigm is allocating optimal resources to VNFs. Most existing work assumes that services are represented as service functional chains (SFC), which are chains. However, for more complex and diversified network services, a more appropriate representation is Virtual Network Function Forwarding Graph (VNF-FG), namely directed acyclic Graph (DAGs). Previous works failed to take advantage of this special graph structure, which makes them unsuitable for complex and diverse network service scenarios. Aiming at the problem of virtual network function Forwarding Graph mapping (VNF-FGE) represented by DAG, this paper proposes a virtual network function (VNF) deployment algorithm based on graph convolution network (GCN) and deep reinforcement learning (DRL), called GDRL-VNFP. First, we describe the VNF-FGE problem as an integer linear programming (ILP) problem and the virtual network service request (NSR) as a DAG. Secondly, in order to overcome the challenges brought about by the different sizes and dynamic arrival of the DAG representation of NSR, an efficient algorithm based on GCN and DRL is proposed, which can generate deployment solutions in real time under the premise of meeting the quality of service and minimize the total cost of deployment. We use GCN to extract features from the physical network topology and VNF-FG represented by DAG, and in addition, and construct a sequence-to-sequence model based on the attention mechanism for the output mapping scheme. Finally, we trained the model using a policy-based gradient-based reinforcement learning algorithm that could quickly find a near-optimal solution for the problem instance. Simulation results show that our GDRL-VNFP outperforms state-of-the-art solutions in terms of deployment cost, service request reception rate and runtime. [ABSTRACT FROM AUTHOR]

The advent of Network Function Virtualization (NFV) has enabled the flexible provisioning of services on Fog-Cloud Computing-based Networks (CFCN) and has facilitated the implementation of 5G networks. NFV transforms hardware middleboxes into sets of software-based Virtual Network Function (VNF) that can host the growing demand for latency-sensitive services at the FCCN. Latency-sensitive and complex services can be provided by composing multiple VNF instances in the Service Function Chain (SFC). VNF instances can be deployed as virtual machines on FCCN components. In general, finding the optimal solution for placement of SFC requests based on VNF instances on FCCN is known as an NP-Hard problem. Dynamic placement of SFCs by reusing VNF instances can improve resource utilization and save time. In this paper, Dynamic SFC placement with VNF reuse (DSPVR) algorithm in FCCN using Deep Reinforcement Learning (DRL) is proposed. DSPVR is a dynamic planning model for SFC placement based on the preliminary VNFs reuse that can reconcile between Quality of Service (QoS) and service costs under FCCN constraints. DSPVR is based on DRL and has been developed with the purpose of maximizing long-term cumulative reward (LTCR). In addition, the DSPVR includes an SFC queue network for efficient distribution of VNFs required over time, which can affect the routing of future requests placement. The simulation results show the superiority of the proposed DSPVR algorithm compared to state-of-the-art methods such as DRL-SFCP and DDQP. The DSPVR outperforms the DRL-SFCP by 4.9% and 9.2% by DDQP in terms of monetary cost. [ABSTRACT FROM AUTHOR]

The development of Fifth-Generation (5G) mobile communication technology has remarkably promoted the spread of the Internet of Things (IoT) applications. As a promising paradigm for IoT, edge computing can process the amount of data generated by mobile intelligent devices in less time response. Network Function Virtualization (NFV) that decouples network functions from dedicated hardware is an important architecture to implement edge computing, deploying heterogeneous Virtual Network Functions (VNF) (such as computer vision, natural language processing, intelligent control, etc.) on the edge service nodes. With the NFV MANO (Management and Orchestration) framework, a Service Function Chain (SFC) that contains a set of ordered VNFs can be constructed and placed in the network to offer a customized network service. However, the procedure of NFV orchestration faces a technical challenge in minimizing the network cost of VNF placement due to the complexity of the changing effect of traffic volume and the dependency on the VNF relationship. To this end, we jointly optimize SFC design and VNF placement to minimize resource cost while taking account of VNF dependency and traffic volume scaling. First, the problem is formulated as an Integer Linear Programming (ILP) model and proved NP-hard by reduction from Hamiltonian Cycle problem. Then we proposed an efficient heuristic algorithm called Traffic Aware and Interdependent VNF Placement (TAIVP) to solve the problem. Compared with the benchmark algorithms, emulation results show that our algorithm can reduce network cost by 10.2% and increase service request acceptance rate by 7.6% on average. [ABSTRACT FROM AUTHOR]

Low-speed internet can negatively impact incident response by causing delayed detection, ineffective response, poor collaboration, inaccurate analysis, and increased risk. Slow internet speeds can delay the receipt and analysis of data, making it difficult for security teams to access the relevant information and take action, leading to a fragmented and inadequate response. All of these factors can increase the risk of data breaches and other security incidents and their impact on IoT-enabled communication. This study combines virtual network function (VNF) technology with software -defined networking (SDN) called virtual network function software-defined networking (VNFSDN). The adoption of the VNFSDN approach has the potential to enhance network security and efficiency while reducing the risk of cyberattacks. This approach supports IoT devices that can analyze large volumes of data in real time. The proposed VNFSDN can dynamically adapt to changing security requirements and network conditions for IoT devices. VNFSDN uses threat filtration and threat-capturing and decision-driven algorithms to minimize cyber risks for IoT devices and enhance network performance. Additionally, the integrity of IoT devices is safeguarded by addressing the three risk categories of data manipulation, insertion, and deletion. Furthermore, the prioritized delegated proof of stake (PDPoS) consensus variant is integrated with VNFSDN to combat attacks. This variant addresses the scalability issue of blockchain technology by providing a safe and adaptable environment for IoT devices that can quickly be scaled up and down to pull together the changing demands of the organization, allowing IoT devices to efficiently utilize resources. The PDPoS variant provides flexibility to IoT devices to proactively respond to potential security threats, preventing or mitigating the impact of cyberattacks. The proposed VNFSDN dynamically adapts to the changing security requirements and network conditions, improving network resiliency and enabling proactive threat detection. Finally, we compare the proposed VNFSDN to existing state-of-the-art approaches. According to the results, the proposed VNFSDN has a 0.08 ms minimum response time, a 2% packet loss rate, 99.5% network availability, a 99.36% threat detection rate, and a 99.77% detection accuracy with 1% malicious nodes.

Network slicing shows promise as a means to endow 5G networks with flexible and dynamic features. Network function virtualization (NFV) and software-defined networking (SDN) are the key methods for deploying network slicing, which will enable end-to-end (E2E) isolation services permitting each slice to be customized depending on service requirements. The goal of this investigation is to construct network slices through a machine learning algorithm and allocate resources for the newly created slices using dynamic programming in an efficient manner. A substrate network is constructed with a list of key performance indicators (KPIs) like CPU capacity, bandwidth, delay, link capacity, and security level. After that, network slices are produced by employing multi-layer perceptron (MLP) using the adaptive moment estimation (ADAM) optimization algorithm. For each requested service, the network slices are categorized as massive machine-type communications (mMTC), enhanced mobile broadband (eMBB), and ultra-reliable low-latency communications (uRLLC). After network slicing, resources are provided to the services that have been requested. In order to maximize the total user access rate and resource efficiency, Dijkstra’s algorithm is adopted for resource allocation that determines the shortest path between nodes in the substrate network. The simulation output shows that the present model allocates optimum slices to the requested services with high resource efficiency and reduced total bandwidth utilization.

Objectives:With the emergence and development of new network structures such as polymorphic network,the demand for network resource capacity is becoming more and more diverse.It is very difficult to adjust the distribution of virtual network functions (VNFs) carried on network slices for on-demand,dynamic and efficient resource capacity matching.This paper uses a data-driven VNF resource capacity prediction method to explore the pre-deployment of VNFs carried on network slices. Methods: Using the data-driven idea, a VNF resource capacity prediction method based on spatiotemporal feature extraction was adopted to pre-deploy VNFs for the upcoming slicing demand through resource capacity prediction. First, the time series of data stream used for prediction is subjected to two-stage weighting processing,and then the processed time series and its dependent spatial topology information are input into the network model for spatiotemporal feature extraction. For the extraction of spatial features, by given an adjacency matrix and a feature matrix,graph convolutional network is used to reorganize the spatial distribution features of time series in the Fourier domain. For the extraction of temporal features, the temporal dependencies of the input data are perceived through the information transfer between the units via gated recurrent units.Then,based on the mapping relationship between the data flow sequence and the number of VNF instances, the feedforward neural network performs data dimension transformation and finally outputs the VNF resource demand prediction results. Results: From the experimental results, the prediction performance of this method is mainly reflected in the following aspects:1.The prediction accuracy of this method is stable.The primary reason is that the method adopts a reasonable spatiotemporal feature extraction structure, which can effectively deal with both the spatial structure with non-Euclidean features and the time series features with contextual dependencies.The secondary reason is that the weighted preprocessing process of the input data stream sequence in this method effectively avoids the characteristic mutation caused by the burst flow in the network,and can truly reflect the changing trend of the data stream and slicing capacity requirements, thus obtaining stable prediction effect. 2. The method has the ability of spatiotemporal prediction. It shows that after a large amount of data training, the spatial feature extraction layer of the method can quickly calculate the topology relationship and data flow distribution of the network, and the temporal feature extraction layer can assist in predicting the sudden changes may occur in the data flow according to the potential correlation between data flows between nodes. The two feature extraction layers work in coordination to obtain accurate spatiotemporal prediction results.3.The method has the ability to convert data flow prediction results. It shows that this method can combine the mapping relationship between the fluctuation trend of data flow and the change trend of the number of VNF instances, and efficiently realizes the conversion of data flow prediction and network slice capacity prediction through the transformation of data dimension. Conclusions: With the vigorous development of artificial intelligence technology, polymorphic network has given computing, storage and transmission capabilities to virtual nodes on network slices,and has been able to realize the dual improvement of network resource utilization and user experience through the adaptive flow of business demand data on the basis of autonomously sensing data throughput and autonomously predicting the resource requirements of VNFs on nodes. With the help of machine learning algorithms, the VNF resource capacity demand prediction method VNFPre proposed for polymorphic network scenarios,it can judge the future VNF resource capacity demand of network slices, and provide a priori information for the placement and mapping of VNFs carried by network slices.

The elastic optical network (EON) adopting virtual network function (VNF) is a new type of network, in which the routing, spectrum, and data center allocation are key and challenging problems, and solving these three problems simultaneously can not only improve the network efficiency for network providers, but also let users obtain better service. However, few existing works handle these three problems simultaneously. To tackle the three problems simultaneously, given a set of network function chains (i.e., a set of tasks), we set up a new multi-objective optimization model in which the total length of paths for all tasks is minimized, the totally occupied spectrums are minimized, and the loads on all data centers are most balanced, simultaneously. To solve the model, we design two new evolutionary algorithms. The experiments are conducted on 16 cases of 4 widely used types of networks, and the results indicate that the proposed model and algorithms are effective. [ABSTRACT FROM AUTHOR]

Bitcoin introduced a cryptocurrency as a form of public ledger consequently that turned into a most popular security technology, Blockchain. Its integrated mining technology lies the key security mechanism. The system allows forming a pool mining group to solve a particular job and share their revenues to their CPU usage while one of them successfully mines a block. To mine a block, a cryptographic puzzle should be solved, which requires significant compute resources that cause huge energy consumption. On the other hand, recent statistics show that low computational energy-restricted Internet of Things (IoT) devices are increasing exponentially. Although it has low energy and limited computation power, it is large in quantity when it is integrated. So we focus on a stochastic geometry theory, which resolves the issue of block mining computation via utilizing multiple mobile IoT devices, given that these IoT devices are Computation Capable Nodes (CCNs). To further normalize this issue, we propose an efficient mathematical solution that uses smart coordination of Virtual Network Functions (VNFs) for IoT devices to enable their CPU usage efficiently. At the same time, the work and credit point distribution policy is smartly handled through virtual pool mining. The proposal renders Network Function Virtualization technology to configure VNF, and Service Function Chain technology is utilized to enable the network flow of such VNFs. New algorithms are presented to solve multiple issues like node discovery, computation offloading, and work credit point distribution. Our goal is to minimize energy consumption within the given time constraint. Implementation results show that although virtual functions for block mining require extensive computations in IoT devices, dividing computation work into small fractions called tasks embedded with VNF, and offloading them to nearby CCNs, tend to minimize the cost and energy consumption of individual shared miners. The overall mining process is proved efficient and faster.

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Network function virtualization (NFV) makes the realization of specific network functions no longer depend on inherent hardware by executing virtual network functions (VNFs), but realizes network functions in a more flexible programming manner, thereby reducing the pressure of resource allocation on the underlying network. Service function chain (SFC) is composed of a set of fixed order VNFs. These VNFs need to be deployed on appropriate physical nodes to meet user function requirements, i.e., the placement of SFC. Traditional solutions mostly use mathematical models or heuristic methods, which are not applicable in the context of large-scale networks. Secondly, the existing methods do not integrate intelligent learning algorithms into the service function chain placement (SFCP) problem, which limits the possibility of obtaining better solutions. This paper presents a multi-objective optimization service function chain placement (MOO-SFCP) algorithm based on reinforcement learning (RL). The goal of the algorithm is to optimize the resource allocation mode, including several performance indexes such as underlying resource consumption revenue, revenue cost ratio, VNF acceptance rate and network latency. We model the SFCP as a Markov decision process (MDP), and use a two-layer policy network as an intelligent agent. In the training stage of RL, the agent comprehensively considers the optimization objectives and formulates the optimal physical node mapping strategy for VNF requests. In the test phase, the whole SFCP is completed according to the trained node mapping strategy. Simulation results show that the algorithm proposed in this paper has excellent performance in the aspects of underlying resource allocation revenue, VNF acceptance rate and so on. In addition, we prove that the algorithm has good flexibility by changing the delay constraint. [ABSTRACT FROM AUTHOR]

This paper proposed an optimized mapping algorithm for service function chain based on improved sparrow search algorithm to realize the low-cost and efficient deployment of service chain in 5G network scenario. It coded the composition schemes and the mapping schemes of service requests together by a double-layer coding method in the mapping process, and then used an improved sparrow search algorithm to obtain the service function chain deployment scheme of the request and calculated the mapping weight. When there were multiple request types in the same time slice, it sorted all service function chains according to the mapping weight,and mapped the requests that had high weight as priority,so as to reduce the delay overhead and improve the utilization of node computing resources and link resources. Simulation results show that the algorithm can effectively reduce deployment overhead and improve resource utilization. [ABSTRACT FROM AUTHOR]

Network function virtualization (NFV) is a promising technology to make 5G networks flexible and agile. NFV decreases operators’ OPEX and CAPEX by decoupling the physical hardware from the functions they perform. In NFV, users’ service request can be viewed as a service function chain (SFC) consisting of several virtual network functions (VNFs) which are connected through virtual links. Resource allocation in NFV is done through a centralized authority called NFV Orchestrator (NFVO). This centralized authority suffers from some drawbacks such as single point of failure and security. Blockchain (BC) technology is able to address these problems by decentralizing resource allocation. The drawbacks of NFVO in NFV architecture and the exceptional BC characteristics to address these problems motivate us to focus on NFV resource allocation to users’ SFCs without the need for an NFVO. To this end, we assume there are two types of users: users who send SFC requests (SFC requesting users) and users who perform mining process (miner users). For SFC requesting users, we formulate NFV resource allocation (NFV-RA) problem as a multi-objective problem to minimize the energy consumption and utilized resource cost, simultaneously. To address this problem, we propose an Approximation-based Resource Allocation algorithm (ARA) using Majorization-Minimization approximation method to convexify NFV-RA problem. Furthermore, due to the high complexity of ARA algorithm, we propose a low complexity Hungarian-based Resource Allocation (HuRA) algorithm using Hungarian algorithm for server allocation. Through the simulation results, we show that our proposed ARA and HuRA algorithms achieve near-optimal performance with lower computational complexity. Also, ARA algorithm outperforms the existing algorithms in terms of number of active servers, energy consumption, and average latency. Moreover, the mining process is the foundation of BC technology. In wireless networks, mining is performed by resource-limited mobile users. Since the mining process requires high computational complexity, miner users cannot perform it alone. So, in this article, we assume that miner users can perform mining process with participating of other users. For mining process, the problem of minimizing the energy consumption and cost of users’ processing resources is formulated as a linear programming problem that can be optimally solved in polynomial time. [ABSTRACT FROM AUTHOR]

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The abstraction of the network node functions using virtualization methods introduced an innovative architecture called Network Function Virtualization (NFV). In NFV, every virtualization software hosts a network service recognized as a Virtual Network Function (VNF). In general, the network provider creates a Service Function Chain (SFC) for every sequence of multiple requested VNFs by the customers. Although NFV allows for a more flexible and economical approach, it is more prone to error and failure. Therefore, providing reliable provisioning for VNF chaining is one of the key issues in NFV. In this paper, we present a systematic literature review to study the pioneer research efforts that provide reliable provisioning for VNF chaining by guaranteeing the availability of the service and resource optimization. Our review is the result of the analysis of 21 screened papers. This paper presents the result of our analysis, including different aspects of a reliable provisioning algorithm, various adopted techniques for reliable provisioning, and the superiority and drawbacks of each algorithm based on the proposed criteria for the evaluation of the provisioning algorithms.

This paper focuses on reducing execution delays of dynamic computing tasks in UAV-assisted fault-prone mobile edge computing (FP-MEC) systems, which combine mobile edge computing (MEC) and network function virtualization (NFV) technologies. FP-MEC is suited to meet Industrial Internet (IIN) requirements such as data privacy, low latency, and low-cost industrial scalability in specific scenarios. However, the reliability of virtual network functions (VNFs) deployed on UAVs could impact system performance. Thus, this paper proposes the dynamic task scheduling optimization algorithm (DTSOA) based on deep reinforcement learning (DRL) for resource allocation design. The formulated execution delay optimization problem is described as an integer linear programming problem and it is an NP-hard problem. To overcome the intractable problem, this paper discretizes it into a series of single-time slot optimization problems. Furthermore, the experimental rigor is improved by constructing a real-time server state update system to calculate the real-time server load situation and crash probability. Theoretical analysis and experiments show that the DTSOA has better application prospects than Q-learning and the recent search method (RSM), and it is closer to the traversal search method (TSM).

Network function virtualization (NFV) is an emerging technology that is becoming increasingly important due to its many advantages. NFV transforms legacy hardware-based network infrastructure into software-based virtualized networks. This transformation increases the flexibility and scalability of networks, at the same time reducing the time for the creation of new networks. However, the attack surface of the network increases, which requires the definition of a clear map of where attacks may happen. ETSI standards precisely define many security aspects of this architecture, but these publications are very long and provide many details which are not of interest to software architects. We start by conducting threat analysis of some of the NFV use cases. The use cases serve as scenarios where the threats to the architecture can be enumerated. Representing threats as misuse cases that describe the modus operandi of attackers, we can find countermeasures to them in the form of security patterns, and we can build a security reference architecture (SRA). Until now, only imprecise models of NFV architectures existed; by making them more detailed and precise it is possible to handle not only security but also safety and reliability, although we do not explore those aspects. Because security is a global property that requires a holistic approach, we strongly believe that architectural models are fundamental to produce secure networks and allow us to build networks which are secure by design. The resulting SRA defines a roadmap to implement secure concrete architectures. [ABSTRACT FROM AUTHOR]

The new radio technologies (i.e. 5G and beyond) will allow a new generation of innovative services operated by vertical industries (e.g. robotic cloud, autonomous vehicles, etc.) with more stringent QoS requirements, especially in terms of end-to-end latency. Other technological changes, such as Network Function Virtualization (NFV) and Software-Defined Networking (SDN), will bring unique service capabilities to networks by enabling flexible network slicing that can be tailored to the needs of vertical services. However, effective orchestration strategies need to be put in place to offer latency minimization while also maximizing resource utilization for telco providers to address vertical requirements and increase their revenue. Looking at this objective, this paper addresses a latency-sensitive orchestration problem by proposing different strategies for the coordinated selection of virtual resources (network, computational, and storage resources) in distributed DCs while meeting vertical requirements (e.g., bandwidth demand) for network slicing. Three orchestration strategies are presented to minimize latency or the blocking probability through effective resource utilization. To further reduce the slice request blocking, orchestration strategies also encompass a retrial mechanism applied to rejected slice requests. Regarding latency, two components were considered, namely processing and network latency. An extensive set of simulations was carried out over a wide and composite telco cloud infrastructure in which different types of data centers coexist characterized by a different network location, size, and processing capacity. The results compare the behavior of the strategies in addressing latency minimization and service request fulfillment, also considering the impact of the retrial mechanism.

The emergence of network function virtualization (NFV) has revolutionized the infrastructure and service management of network architecture. It allows network operators to reduce costs and improve the agility of network service deployment. But finding the best VNF placement is a well-known NP-complete problem. Hence, many previous studies either formulate the problem as an Integer Linear Programming (ILP) Problem or propose greedy algorithms. However, solving ILP is time-consuming, while greedy algorithms could be far from optimal solutions. As a result, neither ILP nor greedy can make quick and accurate placement decisions for dynamic traffic workloads. Therefore, we propose a hybrid method that uses less time to maximize the overall profit of network service deployment. Our evaluations based on real backbone network traffic and topology show that our hybrid approach can achieve up to 36% profit improvement compared to a pure greedy approach, while achieving x30 times computation time speedup over a pure ILP approach.

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In order to effectively improve the end-to-end service delay of the flow in multi-clusters coexisting mobile edge computing (MEC) network,a virtual network function deployment strategy based on improved genetic simulated annealing algorithm was proposed.The delay of mobile service flow was mathematically modeled through the open Jackson queuing network.After proving the NP attribute of this problem,a solution combining genetic algorithm and simulated annealing algorithm was proposed.In this strategy,the advance mapping mechanism avoids the possibility of network congestion,and the occurrence of local optima was avoided through using the methods of individual judgment and corrective genetic.Extensive simulation was set up to evaluate the effectiveness of the proposed strategy under different parameter settings,such as different volume of requests,different scale of service nodes,different number of MEC clusters,and logical link relationships between virtual network functions.Results show that this strategy can provide lower end-to-end services delay and better service experience for latency-sensitive mobile application.