Network Function Virtualization Research Articles

VNF Migration in Digital Twin Network for NFV Environment

Network Function Virtualization (NFV) allows for the dynamic provisioning of Virtual Network Functions (VNFs), adapting services to the complex and dynamic network environment to enhance network performance. However, VNF migration and energy consumption pose significant challenges due to the dynamic nature of the physical network. In order to maximize the acceptance rate of Service Function Chain Requests (SFCR), and reduce VNF migration and energy consumption as much as possible, we summarize several related factors such as the node hosting state, link hosting state, energy consumption, migrated nodes, and whether the mapping is successful. We define the Markov decision process by considering the factors mentioned above. Next, we design the VNF migration algorithm utilizing actor–critic models, graph convolution networks, and LSTM networks. In order to reduce the risk of trial and error during training and prediction in deep reinforcement learning scenarios, we designed a network architecture based on a digital twin (DT). In simulation experiments, compared with the FF algorithm that greedily selects the first available node, our AC_GCN algorithm significantly improves the acceptance rate of SFC requests by 2.9 times more than the FF algorithm in small topology experiments, and 27 times more than the FF algorithm in large topology experiments. Compared with the deep reinforcement learning (DRL) algorithm, which does not consider all the above factors together, for the small topology experiment, our AC_GCN algorithm outperforms the DRL algorithm in terms of request acceptance rate by 13%, underperforms compared to the DRL algorithm in terms of energy consumption by 3.8%, and underperforms compared to the DRL algorithm in terms of the number of migrated nodes for 22%; for the large topology experiment, our AC_GCN algorithm outperforms the DRL algorithm in terms of the request acceptance rate by 7.7%, outperforms the DRL algorithm in terms of energy consumption by 0.4%, and outperforms the DRL algorithm in terms of the number of migrated nodes by 1.6%.

HADES: An NFV solution for energy-efficient placement and resource allocation in heterogeneous infrastructures

Network Function Virtualization (NFV) aims to replace traditional network functions running in proprietary hardware with software instances (i.e., Virtual Network Functions, VNFs) embedded in general-purpose virtualization solutions. Aware that the transition to a fully virtualized network infrastructure will pay a high energy cost, especially in IoT systems composed of a myriad of devices, energy efficiency is one of the key innovative targets of future networks. Edge computing should be considered in IoT environments to save time and energy by processing data near the producer devices. However, applying NFV in the context of IoT/Edge/Cloud environments complicates the placement of VNFs, due to the inherent heterogeneous nature of such environments and the variety of resource demands. This paper proposes an energy-aware placement of service function chains of VNFs and a resource-allocation solution for heterogeneous edge infrastructures that considers the computation and communication delays according to the VNFs’ location in the infrastructure. The solution has been integrated with the ETSI-sponsored project Open Source Management and Orchestration (OSM) as an extension called HADES, which allows the configuration of VNFs and their subsequent resource allocation and deployment at the edge, minimizing energy consumption and ensuring a quality of service. We have applied the deployment of augmented reality services in real and simulated scenarios. The results show up to a 59% reduction in power consumption and QoS compliance in all scenarios considered compared to default OSM placement and four other allocation policies. We prove that our solution has negligible power overhead, and validate the scalability and applicability of HADES.

Framework for Virtualized Network Functions (VNFs) in Cloud of Things Based on Network Traffic Services

The cloud of things (CoT), which combines the Internet of Things (IoT) and cloud computing, may offer Virtualized Network Functions (VNFs) for IoT devices on a dynamic basis based on service-specific requirements. Although the provisioning of VNFs in CoT is described as an online decision-making problem, most widely used techniques primarily focus on defining the environment using simple models in order to discover the optimum solution. This leads to inefficient and coarse-grained provisioning since the Quality of Service (QoS) requirements for different types of CoT services are not considered, and important historical experience on how to provide for the best long-term benefits is disregarded. This paper suggests a methodology for providing VNFs intelligently in order to schedule adaptive CoT resources in line with the detection of traffic from diverse network services. The system makes decisions based on Deep Reinforcement Learning (DRL) based models that take into account the complexity of network configurations and traffic changes. To obtain stable performance in this model, a special surrogate objective function and a policy gradient DRL method known as Policy Optimisation using Kronecker-Factored Trust Region (POKTR) are utilised. The assertion that our strategy improves CoT QoS through real-time VNF provisioning is supported by experimental results. The POKTR algorithm-based DRL-based model maximises throughput while minimising network congestion compared to earlier DRL algorithms.

Discrete spider monkey optimization algorithm for latency‐sensitive VNF deployment and resource allocation

SummaryThe next‐generation networks are expected to support diverse latency‐sensitive services, that is, auto‐driving, telemedicine, and so on. Deploying these services in current hardware‐based networks faces significant challenges. The emergence of network function virtualization (NFV) and software defined network (SDN) makes network deployment flexible and agile. In order to meet the strict latency constraints, joint VNF deployment and resource allocation problems are needed. In this paper, we consider the resource‐delay interpendency and formulate the joint VNF deployment and resource allocation problems as linear programming (LP) problems. Discrete spider monkey optimization (DSMO) algorithm is proposed to solve this problem, which imitates spider monkeys' fission and fusion social behavior during foraging. The two‐phase decision update mechanism greatly increases the diversity of the spider monkey decision population and promotes the algorithm to perform a global search of the solution space. Evaluation results show that the proposed solutions achieve superior performance in terms of latency, CPU utilization and service acceptance rate.

Network-Compute Co-Optimization for Service Chaining in Cloud-Edge-Radio 5G Networks

Flexible resource management can be facilitated by recent drastic advances in software-defined networking (SDN) and network function virtualization (NFV) technologies. Most of recent studies on resource management have mainly focused on the independent optimization of network function and computing function on top of a single-layer SDN architecture. In this paper, we focus on networking and processing resource orchestration to enhance the performance of network/computing functions in a hierarchical cloud-edge-radio 5G network architecture. Specifically, we propose a dynamic resource management algorithm, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RACER</i> , to process network/computing functions by opportunistically exploiting a tradeoff between the characteristics of the cloud and edge with respect to the amount of available resources and transmission delay. Herein, we decompose an original long-term problem including complex decisions in all network layers (that is, cloud, edge, and radio) into spatio-temporal subproblems without loss of optimality by leveraging Lyapunov optimization. Then, we derive the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RACER</i> algorithm to find the decisions in each layer and each time slot. This online-fashioned algorithm is pragmatic because it not only exploits information obtained from instantaneous time slots, but also operates in a distributed manner with small message exchanges between the different layers. Moreover, we demonstrate the excellent performance improvement of the proposed dynamic and holistic <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RACER</i> algorithm compared to static and independent policies in the 5G network environment.

SAFE: Service Availability via Failure Elimination Through VNF Scaling

Virtualized network functions (VNFs) enable software applications to replace traditional middleboxes, which are more flexible and scalable in the network service provision. This paper focuses on ensuring Service Availability via Failure Elimination (SAFE) using VNF scaling, that is, given the resource requirements of VNF instances, finding an optimal and robust instance consolidation strategy, which can recover from one instance failure quickly. To address the above problem, we present a framework based on rounding and dynamic programming. First, we discretize the range of resource requirements into several sub-ranges, and thus the number of instance types becomes a constant. Second, we further reduce the number of instance types by gathering several small instances into a bigger one. Third, we propose an algorithm built on dynamic programming to solve the instance consolidation problem with a limited number of instance types. Finally, we set up a testbed to profile the functional relationship between the resource and the throughput for different types of VNFs, and conduct simulations to validate our theoretical results according to profiling results. The simulation results show that our algorithm outperforms the standby deployment model by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$27.33\%$</tex-math> </inline-formula> on average in terms of the number of servers required. Furthermore, SAFE has marginal overheads, around <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7.22\%$</tex-math> </inline-formula> , compared to the instance consolidation strategy without VNF backup consideration.

Virtual Network Function Placement Model Considering Both Availability and Probabilistic Protection for Service Delay

This paper proposes a virtual network function (VNF) placement model considering both availability and probabilistic protection for the service delay to minimize the service deployment cost. Both availability and service delay are key requirements of services; a service provider handles the VNF placement problem with the goal of minimizing the service deployment cost while meeting these and other requirements. The previousworks do not consider the delay of each route which the service can take when considering both availability and delay in the VNF placement problem; only the maximum delay was considered. We introduce probabilistic protection for service delay to minimize the service deployment cost with availability. The proposed model considers that the probability that the service delay, which consists of networking delay between hosts and processing delay in each VNF, exceeds its threshold is constrained within a given value; it also considers that the availability is constrained within a given value. We develop a two-stage heuristic algorithm to solve the VNF placement problem; it decides primary VNF placement by solving mixed-integer second-order cone programming in the first stage and backup VNF placement in the second stage. We observe that the proposed model reduces the service deployment cost compared to a baseline that considers the maximum delay by up to 12%, and that it obtains a feasible solution while the baseline does not in some examined situations.

Dynamic service function chain placement with instance reuse in Fog–Cloud​ Computing

The advent of Network Function Virtualization (NFV) technology has brought flexible provisioning to Fog–Cloud Computing-based Networks (FCCNs) for enterprises to outsource their network functions to data center networks. Service Function Chaining (SFC) is a networking concept in NFV by which traffic is steered through an ordered set of Virtual Network Functions (VNFs) composing an end-to-end service. When hundreds of users outsource their network functions to FCCN, the optimal placement of VNFs in the network becomes important for assembling SFCs with the aim of resource utilization efficiency. Motivated by the scalability shortcomings of existing schemes, we propose Deep Reinforcement Learning (DRL)-based approaches by simultaneously considering parallelized SFC and reuse of VNFs to solve this problem, i.e., Asynchronous Advantage Actor–Critic (A3C). A parallelized SFC consists of several sub-SFCs, which can reduce delay and guarantee availability. Also, reuse of preliminary VNFs in SFC placement can improve computation acceleration. The proposed scheme pursues the maximization of the long-term cumulative reward for the trade-off between Quality of Service (QoS) and service cost. The results of the experiments show that the proposed scheme performs better than the state-of-the-art methods.

Enabling Efficient Spatio-Temporal GPU Sharing for Network Function Virtualization

By leveraging standard IT virtualization technology and Commercial-Off-The-Shelf (COTS) servers, Network Function Virtualization (NFV) decouples network functions from proprietary hardware devices for flexible service provisioning. But the potential of NFV is significantly limited by its performance inefficiency. With the unparalleled advantages of multi-core parallelism and high memory bandwidth, Graphics Processing Units (GPUs) are regarded as a promising way to accelerate Virtualized Network Functions (VNF). However, the special architecture of GPU brings new challenges to task scheduling and resource allocation. To this end, we propose a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b> PU <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</b> riented <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</b> patio- <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</b> emporal sharing framework for NFV called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Gost</i> , aiming for GPU based VNF performance promotion. The execution order and GPU resource allocation (i.e., the number of threads) are considered in task scheduling to minimize the end-to-end latency for VNF flows. First, we formulate the task scheduling problem into a nonlinear programming form, and then transform it into an equivalent Integer Linear Programming (ILP) form.The problem is proved as NP-hard. We customize the classical list scheduling algorithm and propose a List Scheduling based Spatio-Temporal GPU sharing strategy (LSSTG), whose achievable worst-case performance is also formally analyzed. We practically implement <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Gost</i> prototype, based on which extensive experiments verify the high performance efficiency of LSSTG compared to state-of-the-art in terms of latency and throughput.

A Solution Supporting Secure Transmission of Big Data

Traditional Internet protocol networks cannot provide the service of selecting a secure path to transmit various types of data with specific security constraints. First, to solve the “secure path transmission” problem, this paper first proposes a search-transmit model for secure network path transmission, i.e., to find a multi-attribute optimized path that meets the specific security requirements in the search phase, and then transmit the packets along the optimized path in the transmission phase. Second, we propose a solution to the search-transmit model. The idea of the solution is to use the particle swarm optimization algorithm to search for a secure path that meets the multi-attribute requirements and then set the source route on the router to control the packet transmission. Finally, a prototype system based on network function virtualization is developed to evaluate the feasibility and performance of the proposed solution. Experimental results show that the proposed solution outperforms existing algorithms in terms of performance.

Understanding NFV-Enabled Vehicle Platooning Application: A Dependability View

This paper aims to use analytical modeling technique to quantitatively study the dependability of Vehicle Platooning Application, which consists of Multiple Sub-Services (VPP-MSS) to achieve its functionality. Each sub-service (SS), based on network function virtualization technology, is executed in a container. Both SSes and OSes which SSes run on can suffer from software aging after a long and continuous running, reducing VPP-MSS dependability. Rejuvenation techniques are usually used to combat software aging, but they require the support of backup components. Quantitative study of VPP-MSS dependability enables in-depth understanding of the effectiveness of rejuvenation techniques based on analytical models. In contrast to the existing studies, we develop a semi-Markov process (SMP) model to jointly analyze the impact of rejuvenation technique trigger intervals (RTTIs), backup components' behaviors, time-dependent interactions between various behaviors and the number of active SSes deployed on an OS on the effectiveness of rejuvenation technique. Sensitivity analysis helps identify key parameters for improving the dependability of VPP-MSS. Extensive numerical experiments demonstrate the necessity of considering backup components' behaviors and investigating non-exponentially distributed failure times. We also determine both the optimal RTTI combination and the optimal combination of SSes and OSes, which can maximize VPP-MSS dependability.

Large-Scale Service Function Chaining Management and Orchestration in Smart City

In the core networking of smart cities, mobile network operators need solutions to reflect service function chaining (SFC) orchestration policies while ensuring efficient resource utilization and preserving quality of service (QoS) in large-scale networking congestion states. To offer this solution, we observe the standardized QoS class identifiers of smart city scenarios. Then, we reflect the service criticalities via cloning virtual network function (VNF) with reserved resources for ensuring effective scheduling of request queue management. We employ graph neural networks (GNN) with a message-passing mechanism to iteratively update hidden states of VNF nodes with the objectives of enhancing allocation of resource blocks, accurate detection of availability statuses, and duplication of heavily congested instances. The deployment properties of smart city use cases are presented along with their intelligent service functions, and we aim to activate a modular architecture with multi-purpose VNFs and chaining isolation for generalizing global instances. Experimental simulation is conducted to illustrate how the proposed scheme performs under different congestion levels of SFC request rates, while capturing the key performance metrics of average delay, acceptance ratios, and completion ratios.

Network management schemes for IoT environment towards 6G: A comprehensive review

Usage of smart devices, expansion of Internet of Things (IoT) network, and its adoption in variety of applications grows exponentially day-by-day. It leads to various issues such as network privacy, security management, unreliable network management (NM) scheme, resource utilization, energy utilization, decentralization, transparency, and data interoperability. Development of the NM platform/framework for secure data dissemination in IoT networks is tedious due to heterogeneity and imbalanced IoT protocol stack. Recently, traditional network scheduling methods, Software Defined Networks (SDN), Network Function Virtualization (NFV), and hybrid approaches are widely used for managing the IoT network. But, most of these approaches are controlled by a centralized server, which may not be appropriate due to the heterogeneity of the IoT devices. Motivated from the aforementioned discussion, In this paper, we present a systematic survey on various NM schemes used to secure data dissemination for the IoT environment. We present a detailed taxonomy of all the existing NM schemes for IoT networks. Pros and cons of the traditional NM methods applicable for IoT environment are discussed in detail. The paper discusses a state-of-art survey on integrating blockchain (BC) technology in the existing IoT NM techniques. In this paper, our prime focus is to understand the potential of BC for enabling secured IoT management. Finally, a case study on BC implication for Unmanned Aerial Vehicle (UAV) network using 6G is also discussed along with future directions.

VNF-Enabled 5G Network Orchestration Framework for Slice Creation, Isolation and Management

Network slicing is widely regarded as the most critical technique for allocating network resources to varied user needs in 5G networks. A Software Defined Networking (SDN) and Network Function Virtualization (NFV) are two extensively used strategies for slicing the physical infrastructure according to use cases. The most efficient use of virtual networks is realized by the application of optimal resource allocation algorithms. Numerous research papers on 5G network resource allocation focus on network slicing or on the best resource allocation for the sliced network. This study uses network slicing and optimal resource allocation to achieve performance optimization using requirement-based network slicing. The proposed approach includes three phases: (1) Slice Creation by Machine Learning methods (SCML), (2) Slice Isolation through Resource Allocation (SIRA) of requests via a multi-criteria decision-making approach, and (3) Slice Management through Resource Transfer (SMART). We receive a set of Network Service Requests (NSRs) from users. After receiving the NSRs, the SCML is used to form slices, and SIRA and SMART are used to allocate resources to these slices. Accurately measuring the acceptance ratio and resource efficiency helps to enhance overall performance. The simulation results show that the SMART scheme can dynamically change the resource allocation according to the test conditions. For a range of network situations and Network Service Requests (NSRs), the performance benefit is studied. The findings of the simulation are compared to those of the literature in order to illustrate the usefulness of the proposed work.

Intelligent Deployment of Delay-Sensitive Service Function Chain Based on Parallelization and Improved Cuckoo Search Algorithm

As one of the key focuses in 6G research, the space–air–ground integrated network incorporates a variety of technological frameworks. Network function virtualization allows network functions to be deployed on general servers in the form of software and creates a service function chain (SFC) according to user service requirements. In recent years, the deployment of SFC has become popular research due to the increasing demand for low delay in network application scenarios. Low delay is a crucial indicator of the quality of service, especially for delay-sensitive applications. To address this issue, we propose a method for the deployment of delay-sensitive SFC based on parallelization and the improved cuckoo search (ICS) algorithm (DDSSFC-PICS). This method optimizes the composition and deployment of SFC jointly. First, the serial structure of the SFC is transformed into a parallel structure by determining the dependency of virtual network functions, which reduces the length of the SFC and thereby reduces delay. Second, with the optimization goal of minimizing network delay, a parallel SFC deployment model is established under constraints including packet loss rate and resource availability. Finally, the ICS algorithm is applied for optimization, where delay is used as the fitness measure. By improving the Lévy flight step size and drawing inspiration from the whale algorithm, the performance of the cuckoo search (CS) algorithm is enhanced, leading to a further reduction in delay. The simulation results show that using the same CS deployment method, parallelized SFC has a significantly lower delay compared to serial SFC. Furthermore, the DDSSFC-PICS reduces the delay by 22.58% and 19.02%, respectively, compared with the CS deployment and particle swarm optimization SFC deployment methods.

Service Function Chaining to Support Ultra-Low Latency Communication in NFV

Network function virtualization (NFV) has the potential to fundamentally transform conventional network architecture through the decoupling of software from dedicated hardware. The convergence of virtualization and cloud computing technologies has revolutionized the networking landscape, offering a wide range of advantages, including improved flexibility, manageability, and scalability. The importance of network capability in enabling ultra-low latency applications has been greatly amplified in the current era due to the increased demand for emerging services such as autonomous driving, teleoperated driving, virtual reality, and remote surgery. This paper presents a novel and efficient methodology for service function chaining (SFC) in an NFV-enabled network that aims to minimize latency and optimize the utilization of physical network resources, with a specific focus on ultra-low latency applications. In our proposed methodology, we offer flow prioritization and an adjustable priority coefficient factor (µ) to reserve a portion of physical network resources exclusively for ultra-low latency applications in order to optimize the deployment paths of these applications further. We formulate the SFC deployment problem as an integer linear programming (ILP) optimization model. Furthermore, we propose a set of heuristic algorithms that yield near-optimal solutions with minimal optimality gaps and execution times, making them practical for large-scale network topologies. Performance evaluations demonstrate the effectiveness of our proposed methodology in enabling ultra-low latency applications in an NFV-enabled network. Compared to existing algorithms, our proposed methodology achieves notable enhancements in terms of the end-to-end delay (up to 22 percent), bandwidth utilization (up to 28 percent), and SFC acceptance rate (up to 13 percent).

SFC-based multi-domain service customization and deployment

In order to satisfy diversified service demands, future networks are expected to provide higher quality network services, especially in 5G scenarios. Network Function Virtualization (NFV) is a promising architecture to achieve flexible service provisioning by introducing Service Function Chain (SFC) technology. With explosive expansion of network devices and end users, the complex multi-domain network environment and personalized service demands of different users pose serious challenges to efficient service provisioning. To this end, this paper presents a novel SFC-based Multi-domain Service Customization and Deployment framework, MSCD, by blending merits of Software Defined Networking (SDN) and NFV technologies. MSCD framework can automatically customize multi-domain network services based on users’ personalized service preferences, and deploy them in multi-domain networks, to provide customizable multi-domain services for different users. Taking into account user personalized service preferences, resource allocation and VNF dependency constraints, the multi-domain service customization and deployment problem is mathematically formulated as an optimization model with the target of service satisfaction degree maximization. Furthermore, a Greedy strategy based Heuristic Service Customization and dePloyment (GHSCP) algorithm is proposed to customize and deploy the multi-domain services efficiently. Simulation results demonstrate that the proposed GHSCP algorithm is efficient and outperforms comparison algorithms in terms of service acceptance ratio and service satisfaction degree.

Modeling and performance analysis of hybrid systems by queues with setup time

NFV (Network Functions Virtualization) is a technology to provide network services by applying virtualization. While the virtualization technology provides flexible architecture by itself, a specific environment where physical legacy equipment and virtualized machines coexist is also considered to meet a wide range of requirements from not only users but also service providers. Motivated by such hybrid systems, we propose queueing models with two types of service facilities: legacy servers and virtual machines. Key features to focus on are that while legacy servers are always on standby, virtual machines need setup time to be ready for service because they are shutdown to reduce the power consumption if no jobs are waiting. Keeping in mind delay-sensitive real-time services, we evaluate the performance of the queueing models, in particular, the delay and energy efficiency.

A Meta Reinforcement Learning Approach for SFC Placement in Dynamic IoT-MEC Networks

In order to achieve reliability, security, and scalability, the request flow in the Internet of Things (IoT) needs to pass through the service function chain (SFC), which is composed of series-ordered virtual network functions (VNFs), then reach the destination application in multiaccess edge computing (MEC) for processing. Since there are usually multiple identical VNF instances in the network and the network environment of IoT changes dynamically, placing the SFC for the IoT request flow is a significant challenge. This paper decomposes the dynamic SFC placement problem of the IoT-MEC network into two subproblems: VNF placement and path determination of routing. We first formulate these two subproblems as Markov decision processes. We then propose a meta reinforcement learning and fuzzy logic-based dynamic SFC placement approach (MRLF-SFCP). The MRLF-SFCP contains an inner model that focuses on making SFC placement decisions and an outer model that focuses on learning the initial parameters considering the dynamic IoT-MEC environment. Specifically, the approach uses fuzzy logic to pre-evaluate the link status information of the network by jointly considering available bandwidth, delay, and packet loss rate, which is helpful for model training and convergence. In comparison to existing algorithms, simulation results demonstrate that the MRLF-SFCP algorithm exhibits superior performance in terms of traffic acceptance rate, throughput, and the average reward.

Grant-Free SCMA Design Using SDN/NFV With Physical Layer Security

In 5G and beyond, the end-user broadcasts data into the radio domain; therefore, protecting the physical layer security (PLS) is a more challenging issue. Here, Sparse Code Multiple Access (SCMA) handles PLS naturally. Because multiple physical resources are encoded in different codes, these codes cannot be easily decoded without sharing a mother codebook and mapping matrix. However, there are a few studies about security implementation in the physical layer against the challenges of the three subdomains of end users, base stations, and the core network. Moreover, there are two new research questions for resource allocation in SCMA: How do codebooks affect the Bit Error Rate (BER) performance of end-users? In the presence or absence of a jammer or interference, how should codebooks be assigned to users? We believe that it can only be overcome by using some new paradigms together: Software-defined radio (SDR), Software-defined networking (SDN), and network function virtualization (NFV). We consider the PLS implementation on SCMA-based 5G by investigating codebook assignment in the presence of noise/jammer with a novel Quality of Service (QoS) parameter (ϕ), which is the ratio of the received power by the user to overloading (λ). In the SDN/NFV controller, the QoS metric-based codebook assignment algorithm prioritizes users into newly defined PEACE, NORMAL, and WAR states. In the OpenFlow table of SDR, grant-free access can be easily implemented. As a result, the BERs for different overloading are the same for the same ratio of energy rate per bit and spectral noise density. The BER is lower when the mother codebook is 150% than when the codebook is 200%. An existing codebook of one eMBB can be utilized to serve one URLLC user and two MTC users with an acceptable BER.