Network Function Virtualization Research Articles

Impacts of blockchain in software‐defined Internet of Things ecosystem with Network Function Virtualization for smart applications: Present perspectives and future directions

SummaryAs an emerging technology, blockchain (BC) has been playing a promising role in today's software‐defined networking (SDN)‐enabled Internet of Things (IoT) applications. Because of the salient feature of the network function virtualization (NFV) techniques, SDN can ensure an IoT system runs efficiently and smoothly in a cloud‐driven ecosystem. When cloud‐enabled systems encounter immense security and operational challenges caused mainly by third‐party dependency, large‐scale data communication, and maintenance, BC offers effective and robust data transfer solutions without incorporating intermediaries over the distributed network. With the increased SDN‐BC convergence in the IoT domain, the underlying challenges and perspectives deserve proper attention methodically and structurally. From the motivation of addressing such issues, this study provides necessary insights to combine those for successful plug‐and‐play. Therefore, the study includes purposefully investigating current state‐of‐the‐art to extract the research trends, future directions, and perspectives in this domain. This study provides a comprehensive survey of IoT, SDN, NFV, and BC‐enabled emerging technologies. More importantly, the authors intelligently integrated the four different technologies—IoT, SDN, BC, and NFV based on characteristics, scopes, challenges, taxonomies, and tables in numerous areas. Initially, the authors introduce the SDN‐IoT ecosystem in brief and address the features and applications. We took a close look at the SDN's overall taxonomy based on security, environment, scopes, and challenges. We also briefly describe the integration of SDN‐IoT with the NFV ecosystems. Moreover, we review the prospect of BC technology from security perspectives, its extent, challenges of practical implementation, and the possible integration of IoT regarding smart applications. Finally, this study highlights several future directions based on these technologies.

PALANTIR: An NFV-Based Security-as-a-Service Approach for Automating Threat Mitigation.

Small and medium enterprises are significantly hampered by cyber-threats as they have inherently limited skills and financial capacities to anticipate, prevent, and handle security incidents. The EU-funded PALANTIR project aims at facilitating the outsourcing of the security supervision to external providers to relieve SMEs/MEs from this burden. However, good practices for the operation of SME/ME assets involve avoiding their exposure to external parties, which requires a tightly defined and timely enforced security policy when resources span across the cloud continuum and need interactions. This paper proposes an innovative architecture extending Network Function Virtualisation to externalise and automate threat mitigation and remediation in cloud, edge, and on-premises environments. Our contributions include an ontology for the decision-making process, a Fault-and-Breach-Management-based remediation policy model, a framework conducting remediation actions, and a set of deployment models adapted to the constraints of cloud, edge, and on-premises environment(s). Finally, we also detail an implementation prototype of the framework serving as evaluation material.

Multiple-Walrasian Auction Mechanism for Tree Valuation Service in NFV Market

As a new and effective technology, network function virtualization (NFV) manages network nodes to improve service performance for the next-generation network. Despite its promised effectiveness, efficiently deploying the functions to different consumers is the major difficulty presented in NFV. In this article, the multiple-Walrasian auction graphic model combined with virtual network functions of different bundled tree nodes based on Vickrey–Clarke–Grove (VCG) payment is proposed to maximize the social effectiveness of NFV. It is the first work to define the virtualized service as a tree valuation in the NFV market. To solve this problem, novel algorithms, including the valuation structure algorithm and auction strategy, are implemented along with the NFV to schedule network resources. With all theories considered, we conducted a comprehensive simulation to verify the tree valuation mechanism. The results confirmed that the tree valuation mechanism outperforms the backpack auction model and reserve auction model with respect to social welfare.

Performance Tuning via Lean Measurements for Acceleration of Network Functions Virtualization

Network Functions Virtualization (NFV) replaces the specialized hardware with the software-based forwarding to promise the flexibility, scalability and automation benefits. With an increasing range of applications, NFV must ultimately forward packets at rates that are comparable to the native and specialized hardware-based approaches. However, the transition packet forwarding from specialized hardware to software-based has turned out to be more challenging than expected. Thus, NFV acceleration is desperately needed to play a crucial role in the development of NFV. It is an interesting issue how to address the persistent performance tuning in a way that provides far greater flexibility to meet the demands of power. The existing developments are very inefficient, since that the uncontrollable and unanticipated performance regressions frequently occur. Besides, the environments for full system simulations are traditionally expensive and time consuming to evaluate the system performance. In this paper, we propose the methodology named as “NFV Acceleration via Lean Measurements (NALM)” to tune the performance for the NFV acceleration. NALM provides a holistic measurement approach through combining individual measures to quickly identify the bottlenecks, which can help developers with a better understanding of the design tradeoffs. Moreover, the environments for large scale performance simulation are replaced by a debugger. Thus, the waste is eliminated in terms of time consumption and infrastructure costs of the full system simulation. The systematic analysis of the multi-cores speedup ratio highlights the potential optimization space and rules. We further propose the improvement recommendations on efficient practices. The experiments evaluate the specific effects, and the relationship between the metrics and forwarding performance.

Deep Reinforcement Learning Approaches to Network Slice Scaling and Placement: A Survey

Network slicing in 5G and beyond networks allows the network to be customized for each application or service, by chaining together different virtualized network functions (VNFs) according to service requirements. The increased flexibility offered by network slicing comes at the cost of complexity in management and orchestration, which cannot be solved by traditional reactive human-in-the-loop solutions. This necessitates minimizing human intervention through the use of artificial intelligence (AI) techniques (zero-touch network management). In particular, the scaling and placement of the chain of VNFs which comprise a network slice is a complex combinatorial optimization problem which is difficult to solve effectively with traditional approaches. Driven by the benefits of deep reinforcement learning (DRL) in solving various combinatorial optimization problems, in this article, we survey various DRL-based approaches to slice scaling and placement, including different ways to model the problem and benefits of various DRL techniques in addressing specific aspects of the problem. Further, we highlight key challenges and open issues in the effective use of DRL for network slice scaling and placement.

Low-Latency Robust Computing Vehicular Networks

In this paper, we propose vehicle-centric networking concept to provide low latency services by designing time compression mechanism for computing programs so that the program execution latency can be significantly reduced. In addition, by jointly tailoring data and control planes we implement a highly responsive and cost-efficient edge service. By leveraging network functions virtualization (NFV) and software defined networking (SDN) technologies we provide a suitable environment for at-edge programmability to customize network functionalities for low latency services in multi-platooning systems for vehicular networks. We develop a responsive edge system by implementing control plane over pre-established access points (e.g., road-side units), allocating redundant resources, and utilizing an adaptive service migration mechanism in order to avoid any degradation of the quality of edge services. We design a robust double Knapsack (RDK) framework, where a spare knapsack of redundant resources is allocated to each plane to cope with the uncertainties of vehicle-provided resources. By combining with VNFs, SDN technology has been used to design a centralized management framework to control and monitor the edge computing in multi-platooning systems.

Hybrid Optimization Assisted Green Power Allocation Model for QoS-Driven Energy-Efficiency in 5G Networks

5G networks are progressing to offer services with high Quality of Service (QoS) features like high user mobility skills, higher data rates, high reliable communiqué, and lower latency. For this, numerous novel settings like integration of Software-Defined Networking (SDN) and Network Function Virtualization (NFV) should be implemented in the network system. Here, this paper plans to design an optimization-based power allocation model to increase the Effective Power Efficiency (EPE) that offers guaranteed QoS over “Single-Input Single-Output (SISO) and Multiple-Input Multiple-Output (MIMO)-channel oriented 5 G networks”. Moreover, the statistical QoS-based green power allotment system was also analyzed to increase the EPE over other MIMO networks. Particularly, this paper makes an attempt to make the power allocation optimal, which directly ensures the 5 G network energy efficiency when guaranteeing QoS. This is considered to be the optimization issue, which is solved by MS-DA. Our suggested approach can be applied to hybrid energy supply networks with various network topologies, such as 5 G networks, which are now the most extensively used, and is capable of achieving a good resolution through iterative convergence.

Application of internet of things-based efficient security solution for industrial

Recent years have seen a rise in demonstrations of the Industrial Internet of Things (IIoT). Creating secure Internet of Things (IIoT) applications has become a major challenge. Because of the constant demand for additional functionality, such as foresight, the number of connected devices in the enterprise facility is continually growing. Certifying a smaller number of signatories provides stronger authentication methods and prohibits trustworthy third parties from being publicly certified among the available encryption instruments. Certain network criteria must be met by the virtual network function (VNFs) used in this arrangement. As a result of this output, the final service may be delivered at the lowest possible cost, including processing time and network delay. According to the results of this study, our solution is better suited to simplifying trust management in IIoT devices. These findings reveal that our proposed confidence model may withstand behavioural shifts in hostile IIoT environments. The experimental results show our proposed method BCEPP has a high computational cost of 95.3%, a low latency ratio of 28.5%, and increased data transmitting rate of 94.1%, enhanced security rate of 98.6%, packet reception ratio of 96.1%, user satisfaction index of 94.5%, probability ratio of 33.8% when compared to other methods.

A Hybrid Edge-Cloud System for Networking Service Components Optimization Using the Internet of Things

The need for data is growing steadily due to big data technologies and the Internet’s quick expansion, and the volume of data being generated is creating a significant need for data analysis. The Internet of Things (IoT) model has appeared as a crucial element for edge platforms. An IoT system has serious performance issues due to the enormous volume of data that many connected devices produce. Potential methods to increase resource consumption and responsive services’ adaptability in an IoT system include edge-cloud computation and networking function virtualization (NFV) techniques. In the edge environment, there is a service combination of many IoT applications. The significant transmission latency impacts the functionality of the entire network in the IoT communication procedure because of the data communication among various service components. As a result, this research proposes a new optimization technique for IoT service element installation in edge-cloud-hybrid systems, namely the IoT-based Service Components Optimization Model (IoT-SCOM), with the decrease of transmission latency as the optimization aim. Additionally, this research creates the IoT-SCOM model and optimizes it to choose the best deployment option with the least assured delay. The experimental findings demonstrate that the IoT-SCOM approach has greater accuracy and effectiveness for the difficulty of data-intensive service element installation in the edge-cloud environment compared to the existing methods and the stochastic optimization technique.

Smart5Grid Solutions for enhanced TSO grid observability and manageability in massive RES penetration environment

Background This article elaborates on Horizon 2020 Smart5Grid Research and Innovation (R&I) project and reports the Smart5Grid Open Experimentation Platform (OEP), including its Open Service Repository (OSR), Verification and Validation (V&V), NetApp Controller (NAC), and Multi-access Edge Computing Orchestrator (MECO), as key results of this project. It then translates those results into energy vertical implications of the Smart5Grid for Transmission System Operators (TSOs), focusing on two particular Use Cases (UCs): UC3 Millisecond Level Precise Distribution Generation Control and UC4 Real-time Wide Area Monitoring pilot demonstrator of 5G virtual PDC capabilities for WAM of end-to-end electricity grids. More specifically, this work exhibits UC3 and UC4 NetApps developed as one of the key project deliverables. All use cases generate openly accessible data, except where specific security restrictions are applicable. Methods The Smart5Grid development methodology is based on the concept of Network Applications (NetApps). Their main mission is to hide the complexity of a 5G telco network for energy application developers in a way that empowers them to develop a NetApp without having to deal with the underlying network. A Virtual Infrastructure Manager (VIM) such as OpenStack or Kubernetes hosts every unit that composes a NetApp. The VIM provides monitoring data to a Network Function Vurtualisation Manager and Orchestrator (NFV MANO) framework, which airs information to the NAC that employs analysis algorithms to propose the optimal Virtual Network Function (VNF) and NetApp placing. A Slice Manager (SM) reserves resources for all these capabilities. Results The Smart5Grid architecture represents the main result that this work delineates in the context of its enhanced 5G telecommunication provider (telco) capabilities for transmission and distribution grids to face and manage more efficiently high Renewable Energy Sources (RES) penetration in a decarbonisation context.

Joint VNF Placement, CPU Allocation, and Flow Routing for Traffic Changes

The emerging network-softwarization technologies, such as software-defined networking and network function virtualization play important roles in 5G communication and future networks. One of the critical challenges of the practical application of the softwarized networks is to appropriately place virtual network functions (VNFs). The underlying resources and traffic requirements are often factored in the previous works of VNF placement. However, VNFs’ dynamic abilities of changing traffic are usually ignored. Resources allocated to VNFs can vary their traffic-change ratios, and the adjustment of resource volumes should be provisioned to support traffic changes. In this work, we pay attention to the joint optimization problem of VNF Placement, CPU Allocation, and flow Routing (VNFPAR) in the scenarios consisting of VNFs that can dynamically change traffic. We employ the logarithmic functions to approximate VNFs’ traffic change relations and formulate VNFPAR as a mixed-integer nonlinear programming (MINLP) problem. We demonstrate that this problem is highly nonconvex and involves highly coupled variables. For small-scale VNFPAR problems, we propose an optimal algorithm based on relaxation and programming to consume the minimum bandwidth resources. Because VNFPAR is NP-hard, to quickly find near-optimal solutions for large-scale VNFPAR problems, we present heuristic algorithms based on multistage greedy and simulated annealing, respectively. Besides, to achieve a tradeoff between solution quality and execution time, we decompose VNFPAR into subproblems and design an alternating optimization-based method. We evaluate our algorithms on real-work topologies and traffic patterns. Extensive simulations show that our proposed heuristic algorithms are convergent, stable, and effective in terms of solving VNFPARs. The proposed algorithms have small optimality gaps within 7.4%–26.3%. Meanwhile, they save 39.3%–48.4% bandwidth resources compared with relevant baseline technologies.

Technological Transformation of Telco Operators towards Seamless IoT Edge-Cloud Continuum.

This article investigates and discusses challenges in the telecommunication field from multiple perspectives, both academic and industry sides are catered for, surveying the main points of technological transformation toward edge-cloud continuum from the view of a telco operator to show the complete picture, including the evolution of cloud-native computing, Software-Defined Networking (SDN), and network automation platforms. The cultural shift in software development and management with DevOps enabled the development of significant technologies in the telecommunication world, including network equipment, application development, and system orchestration. The effect of the aforementioned cultural shift to the application area, especially from the IoT point of view, is investigated. The enormous change in service diversity and delivery capabilities to mass devices are also discussed. During the last two decades, desktop and server virtualization has played an active role in the Information Technology (IT) world. With the use of OpenFlow, SDN, and Network Functions Virtualization (NFV), the network revolution has got underway. The shift from monolithic application development and deployment to micro-services changed the whole picture. On the other hand, the data centers evolved in several generations where the control plane cannot cope with all the networks without an intelligent decision-making process, benefiting from the AI/ML techniques. AI also enables operators to forecast demand more accurately, anticipate network load, and adjust capacity and throughput automatically. Going one step further, zero-touch networking and service management (ZSM) is proposed to get high-level human intents to generate a low-level configuration for network elements with validated results, minimizing the ratio of faults caused by human intervention. Harmonizing all signs of progress in different communication technologies enabled the use of edge computing successfully. Low-powered (from both energy and processing perspectives) IoT networks have disrupted the customer and end-point demands within the sector, as such paved the path towards devising the edge computing concept, which finalized the whole picture of the edge-cloud continuum.

Position-aware packet loss optimization on service function chain placement

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

Service function chain migration with the long-term budget in dynamic networks

Mobile edge computing emerges as a new paradigm to provide low-latency network services in the close proximity to users. Based on the network function virtualization (NFV) technology, network services can be flexibly provisioned as service function chain (SFC) deployed at edge servers. In some scenarios, such as the vehicular or UAV-assisted edge computing, the network topology varies rapidly due to the mobile edge servers, which changes the routing path between adjacent VNFs in an SFC. Migrating SFC to adapt to the frequent topology change can reduce the SFC latency, and improve the quality of users’ experience. However, frequent SFC migration will unavoidably increase the operation cost. In this paper, to optimize the system performance in a cost-efficient manner, we study the SFC migration problem in dynamic networks with a long-term cost budget constraint. We then propose the Topology-aware Min-latency SFC Migration (TMSM) method to strike a desirable balance between the SFC latency and the migration cost. Specifically, we first apply the Lyapunov optimization to decompose the long-term optimization problem into a series of real-time optimization sub-problems. Since the decomposed problem is still NP-hard, a Markov approximation based heuristic is proposed to seek a near-optimal solution for each sub-problem. Compared with the rerouting-only strategy, which does not migrate any VNF, our TMSM reduces the latency by at least 21% on average in each time slot. Extensive evaluations show that the proposed algorithm achieves a better tradeoff between the SFC latency and migration cost than the baselines.

Core-selecting auction-based mechanisms for service function chain provisioning and pricing in NFV markets

With the rapid development of network function virtualization (NFV), an ever-increasing number of enterprises, businesses and operators are resorting to network service provisioning by means of the service function chains (SFCs). Toward this trend, NFV markets are emerging, where users rent SFCs from the network service provider (NSP) to execute their tasks, and the NSP uses the computation resources across geo-distributed clouds to construct various SFCs. Such NFV markets need well-designed pricing mechanisms for SFCs since the users have motivations to strategize around their payments. The Vickrey–Clarke–Groves (VCG) mechanism is a natural fit here, which can ensure that truthful bidding is a dominant strategy. However, VCG mechanism is susceptible to the problems of collusion and shill bidding. To address these problems, in this paper, we design a core-selecting auction-based mechanism (CSAM) for SFC provisioning and pricing in the NFV market. This mechanism considers dynamic provisioning, flexible deployment and reservation prices across geo-distributed clouds. Specifically, we first formulate the core of NFV auction, and prove that an in-core outcome can prevent collusion and shill bidding. Next, we use a couple of correlated linear program (LP) and quadratic program (QP) to design an in-core VCG-nearest payment algorithm, aiming to acquire an in-core outcome and minimize users’ incentives to bid untruthfully. Simulation results verify the strict theoretical analysis, and validate the effectiveness and efficiency of our proposed CSAM.

A migration method for service function chain based on failure prediction

With the deep application of network technologies in different industries, the demand for network services is becoming more and more diversified. Network operation and maintenance are facing severe challenges, which can be solved by network function virtualization (NFV). NFV technology provides services for users through deploying service function chain (SFC) on servers and substrate links. However, once a server fails, the services it hosts will be affected or even interrupted. Therefore, it is very important to predict failures and migrate SFCs in advance according to predicted results. In this paper, we propose a failure prediction method based on the improved long short-term memory neural network (PMILSTM), which employs LSTM to predict failures. To further improve prediction accuracy, the simulated annealing particle swarm optimization algorithm is adopted to optimize the number of neurons in each long short-term memory layer and the time window length. A server may host multiple SFCs. When a server fails, in order to reduce the impact on users, it is necessary to simultaneously migrate all the SFCs hosted by the server. We propose an improved sparrow search algorithm (ISSA) and a service function chain migration method based on the ISSA (MMISSA). The ISSA introduces tent chaos, opposition-based learning, dynamic weight factor, and mutation operation into SSA to achieve the better global optimization ability. The MMISSA method adopts the ISSA to migrate SFCs so that it can simultaneously search for migration servers for all the virtual network functions deployed on a soon-to-fail server. The better global optimization ability of the ISSA enables better migration results. Therefore, the migration success ratio is improved. Moreover, the fitness function simultaneously considers the average migration cost and migration time. As a result, the MMISSA method effectively reduces the migration cost and migration time.

Fast Resource Allocation for Resilient Service Coordination in an NFV-Enabled Internet-of-Things System

Network Functions Virtualization (NFV) is a new way of leveraging an Internet-of-Things (IoT) system to provide real-time and highly flexible service creation. In an NFV-enabled Internet-of-Things (NIoT) system, several IoT functions implemented as Virtual Network Functions can be linked as a service function chain to build a customized IoT service quickly. It is important for an IoT service to be able to recover from a failure. However, the supply of a resilient IoT service in an NIoT system is challenging due to the coordination of distributed VNF instances. In this paper, we formulate the problem of resilient service coordination in an NIoT system as a mixed-integer linear programming model, namely RSOd. The model offers the optimal resource allocation for minimizing service disruption when a failure happens at a node of an NIoT system. We also develop two modified versions of RSOd for different use cases required by an IoT provider. Further, two approximation algorithms are proposed to provide a resilient service for a large-scale NIoT system. The evaluation results show that RSOd and its modified versions produce the optimal resource allocation in significantly reduced time compared to previous work. The results suggest that an IoT provider should carefully select an appropriate resource allocation strategy as it has to pay a resource cost to minimize the service disruption. The results also show that our proposed priority-based heuristic algorithm outperforms an approximation algorithm based on Simulated Annealingin terms of the service disruption and computation time.

Empowering Network Security With Programmable Switches: A Comprehensive Survey

With the growth of network applications such as 5G and artificial intelligence, network security techniques, i.e., the techniques that detect various attacks (e.g., well-known denial-of service (DDoS) attacks) and prevent production networks (e.g., data center networks) from being attacked, become increasingly essential for network management and have gained great popularity in the networking community. Generally, these techniques are built on proprietary hardware appliances, i.e., middleboxes, or the paradigm that combines both software-defined networking (SDN) and network function virtualization (NFV) to implement security functions. However, the techniques built on middleboxes are proven to be hard-to-manage, costly, and inflexible, thereby making them an out-of-date choice in network security. For the techniques built on SDN and NFV, they virtualize and softwarize security functions on commodity servers, leading to non-trivial performance degradation. Fortunately, the recent emergence of programmable switches brings new opportunities of empowering network security techniques with the characteristics of easy-tomanage, low cost, high flexibility, and Tbps-level performance. In this survey, we focus on this promising trend in network security. More precisely, this survey first presents the preliminaries of programmable switches, which are the primary driver of next-generation network security techniques. Next, we comprehensively review existing techniques built on programmable switches, classify these techniques, and discuss their background, motivation, design, implementation, and limitations case-by-case. Finally, we summarize open issues and future research directions in this promising research topic of network security.

SFC Design and VNF Placement Based on Traffic Volume Scaling and VNF Dependency in 5G Networks

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.

Challenges and Mitigation Strategies for Transition from IPv4 Network to Virtualized Next-Generation IPv6 Network

The rapid proliferation of the Internet has exhausted Internet Protocol version 4 (IPv4) addresses offered by Internet Assigned Number Authority (IANA). The new version of the IP i.e. IPv6 was launched by Internet Engineering Task Force (IETF) with new features, such as a simpler packet header, larger address space, new anycast addressing type, integrated security, efficient segment routing, and better Quality of Services (QoS). Virtualized network architectures such as Network Function Virtualization (NFV) and Software Defined Network (SDN) have been introduced. These new paradigms have entirely changed the way of internetworking and provide a lot of benefits in multiple domains of applications that have used SDN and NFV. ISPs are trying to move from existing IPv4 physical networks to virtualized next-generation IPv6 networks gradually. The transition from physical IPv4 to software-based IPv6 is very slow due to the usage of IPv4 addresses by billions of devices around the globe. IPv4 and IPv6 protocols are different in format and behaviour. Therefore, direct communication between IPv4 and IPv6 is not possible. Both protocols will co-exist for a long time during transition despite the incompatibility issues. The core issues between IPv4 and IPv6 protocols are compatibility, interoperability, and security. The transition creates many challenges for ISPs during shifting the network toward a software-based IPv6 network. Packet traversing, routing scalability, the guarantee of performance, and security are the main challenges faced by ISPs. In this research, we focused on a qualitative and comprehensive survey. We summarize the challenges during the transition process, recommended appropriate solutions, and an in-depth analysis of their mitigations during moving towards the next-generation virtual IPv6 network