Network Function Virtualization Research Articles

AI-Enhanced Traffic Prediction and Congestion Control: A Framework for CNF and VNF Networks

This article comprehensively analyzes artificial intelligence-driven approaches to traffic prediction and congestion control in Cloud-Native Functions (CNFs) and Virtual Network Functions (VNFs) based networks. This article examines recent advancements in predictive analytics and dynamic resource allocation, focusing on implementing deep learning frameworks such as Long Short-Term Memory (LSTM) networks for traffic pattern forecasting. This article demonstrates how continuous learning models and real-time telemetry data integration enable adaptive network responses to fluctuating traffic conditions. This article indicates that AI-enhanced load balancing and traffic shaping techniques significantly improve network performance, achieving a more efficient distribution of resources across network nodes while maintaining consistent Quality of Service (QoS). This article highlights the transformative potential of these innovations in meeting the demanding requirements of 5G networks and beyond, offering insights into cost-effective resource management strategies and scalable network solutions. Experimental results show substantial improvements in latency reduction and resource utilization efficiency, presenting a promising direction for next-generation telecom service optimization.

A New English Education Model Based on 6G and Sliced Network Virtual Reality Platform

ABSTRACTThe information society has led to a shift in traditional English education methods, with the evolution of technology, particularly internet and communication network technologies, reshaping the teaching landscape. This facilitated innovative instructional approaches and enhanced the learning experience. This research introduces a novel virtual learn net architecture (VLNA) within the 6G network layers, which processes the performance of the virtual reality‐based English education system (VR‐EES) model to provide a seamless, personalized learning experience for online learners. This architecture is structured into several layers: The user equipment (UE) layer connects VR headsets to the network with ultrareliable, low‐latency links; the radio access network (RAN) layer, employing massive MIMO and beam forming, enhances connection speed, capacity, and coverage. Edge computing handles latency‐sensitive tasks like speech recognition and adaptive content delivery, reducing the load on the core network. The core network layer (CLN) manages network slices for specific learning tasks such as real‐time interaction, high‐definition multimedia, and computation‐intensive processes, with control plane and user plane separation (CUPS) optimizing network management and security through end‐to‐end encryption. Software‐defined networking (SDN) and network function virtualization (NFV) provide centralized, dynamic control, allowing real‐time resource allocation based on demand. Cloud‐edge integration supports Artificial intelligence (AI)‐driven adaptive learning, optimizing educational content delivery based on individual progress. The study results demonstrate that stimulation of VLNA achieved significant improvements in latency reduction, bandwidth utilization, throughput, packet loss rate, jitter, user engagement, learning efficiency, and user satisfaction. The integration of edge computing and network slicing led to a significant reduction in latency, while the enhanced throughput enabled seamless VR experiences. In this study, latency reduction, bandwidth utilization, and user satisfaction emerge as the most significant factors, with user satisfaction standing out as the top performer due to its substantial impact on enhancing the overall learning experience. The packet loss rate is maintained to a certain level, ensuring reliable data transmission. The VR‐EES model's experimental results also enhanced visual learning, multimedia quality, user pleasure, learning effectiveness, and user engagement.

Cross-Level Security Verification for Network Functions Virtualization (NFV)

Cross-Level Security Verification for Network Functions Virtualization (NFV)

End-to-end latency upper bounds and service chain deployment algorithm based on industrial internet network

The diverse service requests in industrial Internet networks require flexible and efficient service chain deployment to ensure the quality of service (QoS). However, current deployment algorithms for service chains are primarily designed to guarantee only low end-to-end latency; they often overlook the amount of service chains that can be accommodated by the network and could lead to severe network load imbalances, significantly reducing service efficiency and causing serious network congestion issues. To address the above issues, we develop a mathematical model of the network topology and service request chains by integrating Network Function Virtualization (NFV) and Software Defined Networking (SDN). Utilizing network calculus theory, we derive the upper bound of end-to-end delay for service chain routing and analyzed the relationship between the upper bound of service chain routing delay and the resource allocation of Virtual Network Function (VNF) nodes. Based on the aforementioned model, we propose a novel service chain deployment algorithm named the Delay-Aware Load-Balanced Routing Algorithm (DLBRA). DLBRA comprehensively considers network traffic load balancing and end-to-end latency of service chains, rationally allocating VNF node resources to complete the determined service chain routing deployment. Experimental results indicate that, compared to the shortest path and load balancing algorithms, DLBRA not only ensures that the end-to-end delay of the service chain meets its QoS requirements, but also effectively reduces network load imbalance, significantly increasing the number of service chain requests that the network can accommodate. Additionally, DLBRA provides tailored deployment guidance for different types of service chains, such as latency-sensitive and data-intensive service chains, ensuring optimal utilization of network resources. This algorithm enhances the efficiency of service chain deployment in industrial internet scenarios and possesses broad application potential in other network environments where delay optimization and load balancing are critical, such as intelligent transportation, cloud computing, and 5G networks.

Security implication of network slicing in 5g-Enabled IoT environment

The Internet of Things (IoT) situation has changed drastically as a result of the deployment of 5G technology, that provides improved connectivity, scalability, and network slicing for service differentiation. The security implications of network slicing in 5G-enabled IoT systems are examined in this study, with a focus on the necessity of strong isolation measures. The contribution of important technologies like Network Function Virtualization (NFV) and Software-Defined Networking (SDN) to safe slice operation is assessed. The limitations of current methods are also addressed by a Dynamic Secure Isolation Framework (DSIF), which combines dynamic resource allocation, AI-driven threat detection, and Zero Trust Architecture to improve isolation and reduce vulnerabilities. The investigation suggests new technologies like blockchain and quantum cryptography as viable solutions for the primary issues with secure slicing, including as cost, latency, and lifecycle complexity. The suggested framework lays the groundwork for upcoming IoT advancements by guaranteeing safe, scalable, and resilient slice operations. Proactive security measures should be prioritized, standardized standards should be developed together, and more research into economic isolation methods should be conducted. The security and dependability of 5G network slicing in IoT are improved by this study's findings.

The Promise of Applying Machine Learning Techniques to Network Function Virtualization

Article The Promise of Applying Machine Learning Techniques to Network Function Virtualization Houda Jmila 1, Mohamed Ibn Khedher 2,*, and Mounim A. El-Yacoubi 3 1 Institute LIST, CEA, Paris-Saclay University, 91190 Palaiseau, France 2 IRT-SystemX, 2 Bd Thomas Gobert, 91120 Palaiseau, France 3 Samovar, Telecom SudParis, Institut Polytechnique de Paris, 19 place Marguerite Perey, 91120 Palaiseau, France * Correspondence: mohamed.ibn-khedher@irt-systemx.fr Received: 28 December 2023 Accepted: 15 August 2024 Published: 24 December 2024 Abstract: “Network Function Virtualization” (NFV) is an emerging technology and 5G key enabler. It promises operating expenditure savings and high flexibility in managing the network by decoupling the network functions, like firewalls, proxies etc., from the physical equipments on which they run. In order to reap the full benefits of NFV, some challenges still need to be overcome, namely those related to resource management, security and anomaly detection. Recently, Machine learning (ML) has been applied in different fields and has demonstrated amazing results. Utilizing Machine learning to address the challenges faced by NFV is a promising research field that requires further investigation. In this paper, we shed light on this domain by discussing the potential and challenges of ML application to NFV and by surveying existing works.

Load-Balanced Dynamic SFC Migration Based on Resource Demand Prediction.

In network function virtualization, the resource demand of network services changes with network traffic. SFC migration has emerged as an effective technique for preserving the quality of service. However, one important problem that has not been addressed in prior studies is how to manage network load while maintaining service-level agreements for time-varying resource demands. Therefore, we propose the Resource Predictive Load Balancing SFC Migration (RP-LBM) algorithm in this paper. The algorithm uses CNN-AT-LSTM to predict VNF resource demands in advance, eliminating the delays associated with dynamic migrations and determining the optimal migration timing. It leverages the PPO algorithm's perceptual capabilities in complex environments to develop SFC migration strategies and ensure network load balancing. Additionally, it reduces the number of subsequent migrations and minimizes the service interruption rate. The simulation results show that the service interruption rate of the RP-LBM algorithm is on average 27.3% lower than that of the passive migration method. The PPO-based migration algorithm has lower SFC migration times and service interruption rates compared to the DQN algorithm, ensuring service continuity with low migration costs.

Increasing Security and Capacity in SDNFV Structures Via Edge Node Extension

The rapid proliferation of IoT devices and corresponding requirements for efficient data processing, Software-Defined Networking Function Virtualization (SDNFV) has come to be a key vehicle for agile management of network resources. The advanced SDNFV model proposed in this study is intended to resolve the two main challenges of security and scalability. The sensitivity of the data transmitted through IoT networks as they grow in size and intricacy requires improved security procedures to hold ill-suited access to their information and ensure its integrity. Encryption & Authentication Protocols: Integration of the encryption and authentication protocol together in a model, that secures data streams against potential cyber threats and threats, enhancing security paradigm. Additionally, the model tackles the scalability challenge by implementing multi-edge node support for distributed processing to better manage high volumes of IoT data. Such expansion is especially notable since it solves latency issues and bottlenecks in the network so a more resilient network structure. The current study compares the simulation results of this improved SDNFV model with existing models and showcases that amidst numerous architectures, the suggested model provides higher efficiency in terms of data privacy and processing capability. This latest development in SDNFV may be fundamental to future IoT platforms that are capable of providing a custom non-functioning IoT backbone to cope with the big data requirements of today's ever-growing network surrounding such devices.

Future-Proof Networks: NFV’S Role In 5g and Beyond

Riding the wave of the multifaceted rapid change in communication technologies and universal deployment of 5G, Network Functions Virtualization, or NFV, has emerged as a disruptive framework for adaptable, scalable, and cost-efficient networks. This can be because NFV decouples network functions from specialized hardware, affording the flexibility that addresses growing demands in ultra-reliable low-latency communications, enhanced mobile broadband, and massive machine-type communications. These capabilities are essential for meeting 5G’s requirements and preparing for the more complex challenges anticipated with 6G. As 6G transition begins, the role of NFV has to be reassessed in order to meet the increased challenges for ultra-low latency, massive connectivity, and unprecedented network reliability. This paper considers the ability of NFV to evolve with new networking standards by unpacking its potentials, limitations, and opportunities for improvement. This research determines factors critical to the scalability, flexibility, and efficiency of NFV in various network scenarios through simulations, case studies, and performance metric analyses. In doing this, it raises the need for advancing NFV architecture to stay relevant. Rather than only looking at current applications, this work addresses new future needs and maybe even answers issues for future stringent network standards that do not compromise on profitability. Such insights will be useful for developing future-proof infrastructures, positioning NFV as a cornerstone in nextgeneration networks such as 6G and beyond.

Analysis of development of fifth generation communication networks

The article is devoted to a comprehensive analysis of key aspects of the development of fifth-generation (5G) networks in Ukraine. Current problems and prospects for the implementation of 5G are studied, taking into account technological, economic and regulatory aspects. Mathematical models and algorithms are proposed for optimizing the allocation of radio frequency spectrum, placement of base stations, implementation of virtualization of network functions Network Function Virtualization (NFV) and software-configured networks Software-Defined Networking (SDN), as well as ensuring quality of service (QoS) for different classes of traffic. The developed solutions allow to increase the efficiency of deployment and operation of 5G networks in conditions of limited resources. A mathematical model of spectrum allocation optimization using linear programming has been developed, which takes into account the efficiency of spectrum use by different operators. To optimize the placement of base stations, an approach based on game theory is proposed, which takes into account bandwidth limitations and placement costs while ensuring the necessary coverage. A mathematical model is presented for optimizing the placement of virtualized network functions on servers with limited resources in order to minimize costs and ensure the required performance. An algorithm based on gradient descent for dynamic resource management and QoS is proposed. Based on a comparative analysis of optimization methods, an integrated approach to solving key problems of 5G implementation is developed. A mathematical model for optimizing the allocation of radio frequency spectrum based on linear programming is proposed, which provides an increase in spectral efficiency by 23–27 % compared to basic methods. A game-theoretic approach to optimizing the placement of base stations is presented, which allows reducing the number of overloaded sectors by 45 % and increasing energy efficiency by 13.3 %. A genetic algorithm for optimizing the placement of virtualized network functions (VNF) is developed, which provides a 21.4 % higher resource utilization. Experimental studies have confirmed that the proposed solutions allow achieving an overall increase in network efficiency by 20–25 % while reducing operating costs by 15–20 %. The results create a methodological basis for effective planning and deployment of 5G networks in Ukraine.

A systematic literature mapping on the security of 5G and vertical applications

Background: The deployment of 5G infrastructure is one of the vectors for new application scenarios since it enables enhanced data bandwidth, low latency, and comprehensive signal coverage. This communication system supports various vertical applications such as smart health, smart cities, smart grids, and transportation systems. However, these applications bring new challenges to 5G networks due to specific requirements for such scenarios. Furthermore, as software-based technologies, including network slicing, software-defined networks, network function virtualization, and multi-access edge computing, are a fundamental part of the 5G architecture, the network can expose these applications to new security and privacy concerns. Results: This study summarizes existing literature on 5G vertical applications security. We highlight vulnerabilities, threats, attacks, and solutions for 5G vertical applications. We conducted a systematic literature mapping to discuss security and privacy challenges regarding the 5G vertical applications. We reviewed 389 papers from 2,349 produced by searching with a curated search query and discussed vulnerabilities, threats, attacks, and solutions for 5G vertical applications. Conclusions: Smart cities, Industry 4.0, smart transportation, public services, smart grids, and smart health are vertical applications with relevant security concerns. We observed the need for more research since the 5G and vertical applications continuously evolve.

Comparison of Open Source SDN Controllers and Cloud Platforms in Terms of Performance, Stability, and Infrastructure Flexibility

The IT industry is advancing rapidly, with virtually every branch of modern computing experiencing swift development. Concepts such as Cloud Computing and Artificial Intelligence no longer surprise anyone. Recently, Software Defined Networks (SDN) have been gaining significant popularity. This innovative approach to computer networks allows for greater flexibility and is, therefore, much more well-known in the world of cloud computing than in traditional network implementations. This paper introduces the concept of SDN and Network Functions Virtualization (NFV) and outlines all the challenges and security issues associated with the cloud environment. The dynamic nature of the IT landscape requires constant adaptation to emerging technologies, and SDN represents a noteworthy evolution in the realm of computer networking. Platforms such as SDN and open-source tools enabling the creation of private cloud environments such as OpenStack or OpenNebula were compared. At the same time, aspects like security, network performance, flexibility, and scalability were analyzed. Based on the prior analysis, a comprehensive cloud environment was built using the OpenStack solution and SDN - OpenDaylight was deployed. Additional tests conducted on the OpenStack cloud, both with and without SDN, demonstrated the superiority of SDN implementation in the cloud.

AI Based Resource Management for 5G Network Slicing: History, Use Cases, and Research Directions

ABSTRACT5G, 6G, and beyond networks promise to support vertical industrial services with strict QoS parameters, but the hardware‐based "one‐size‐fits‐all" model of legacy networks lacks the flexibility needed for diverse services. The foundation of 5G networks lies in softwarization, with network slicing, Software Defined Networking (SDN), and Network Function Virtualisation (NFV) serving as its core components. The network‐slicing‐based shared network environment necessitates an intelligent and flexible resource management approach. In this case, traditional approaches are no longer suitable for dealing with a dynamic network environment. With recent advancements, AI‐based approaches have the potential to manage resources autonomously. This paradigm shift underscores the need for deep and extensive investigation. However, existing literature on this subject is fragmented and lacks a cohesive overview of network slicing. To address these gaps, our review paper aims to provide a comprehensive scope of network slicing in a unified manner. In this sequence at first, this paper presented a conceptual overview of network slicing and enabling technologies, including SDN, NFV, and edge computing. Secondly, this paper identifies the relevant phases of resource management and presents AI‐based resource management for network traffic classification, admission, allocation, and scheduling. Finally, it also discusses the deployment of network slicing‐enabled key use cases and their practical deployment, the research gap, and open research challenges. To the best of our knowledge, this is the first attempt to critically analyze and present a consolidated review of the state of the art in network slicing resource management modules and network slicing‐enabled key industrial use cases. This paper aims to guide researchers in developing innovative solutions and assist network players in the practical deployment of network slices for industrial applications.

Intent‐Based Network Configuration Using Large Language Models

ABSTRACTThe increasing scale and complexity of network infrastructure present a huge challenge for network operators and administrators in performing network configuration and management tasks. Intent‐based networking has emerged as a solution to simplify the configuration and management of networks. However, one of the most difficult tasks of intent‐based networking is correctly translating high‐level natural language intents into low‐level network configurations. In this paper, we propose a general and effective approach to perform the network intent translation task using large language models with fine‐tuning, dynamic in‐context learning, and continuous learning. Fine‐tuning allows a pretrained large language model to perform better on a specific task. In‐context learning enables large language models to learn from the examples provided along with the actual intent. Continuous learning allows the system to improve overtime with new user intents. To demonstrate the feasibility of our approach, we present and evaluate it with two use cases: network formal specification translation and network function virtualization configuration. Our evaluation shows that with the proposed approach, we can achieve high intent translation accuracy as well as fast processing times using small large language models that can run on a single consumer‐grade GPU.

Group search optimization-assisted deep reinforcement learning intelligence decision for virtual network mapping

Virtual network mapping (VNM), as a key technology in network virtualization, has received widespread attention due to its ability to instantiate network services on infrastructure. However, existing VNM technologies have drawbacks, such as poor dynamic mapping processes, single search strategies, and low resource utilization. In this end, we propose a novel group search optimization-assisted deep reinforcement learning (DRL) intelligence decision for virtual network mapping, GSRL-VNM. In this algorithm, we first formalize the deep reinforcement learning model of VNM and describe the dynamic characteristics of VNM process. Then, in order to effectively reduce resource fragmentation and improve the mapping success rate in VNM process, group search optimization (GSO), a swarm intelligent optimization algorithm with excellent global search ability, is utilized to assist deep reinforcement learning intelligent decision-making by improving convergence speed and optimal value. The simulation results show that the proposed GSRL-VNM algorithm outperforms the existing baseline algorithms in terms of acceptance rate, link pressure, long-term average cost, and average revenue.

Scaling and Dynamic Resource Reallocation in NFV

Network Function Virtualization (NFV) has brought incredible experiences for Internet users and network operators. NFV enables the implementation of Virtualized Network Functions (VNFs) as software running in High Volume Servers (HVSs) to execute a Service Function Chain (SFC) to satisfy service demands of Internet users. During the execution of SFCs, VNFs and Virtual Links (VLs) tend to change their resource requirements due to the dynamic nature of the end user's demands. In this paper, we focus on dynamic resource allocation to the elements of SFC throughout the SFC process to adapt to the elasticity in demand from users by providing an overall picture of NFV and the scaling problem of SFC. We then review and analyze related studies on dynamic resource allocation of NFV systems during SFC operation and analyze the results of these projects. The most recent works are also classified based on several criteria to highlight their approaches, achievements, and also shortcomings. Finally, we introduce some research directions to deal with the scaling problem during SFC operation that needs more attention from researchers to inspire future work in the elastic operation of NFV-enabled systems.

A Network Calculus Model for SFC Realization and Traffic Bounds Estimation in Data Centers

Network Function Virtualization (NFV) is a promising technology that can transform how internet service providers deliver their services. However, recent studies have identified several challenges in adopting NFV. Two key challenges are central to the operation and capacity planning of NFV Data Centers (DCs): (i) Service Function Chain (SFC) realization —determining if a new request with a known profile can be accommodated—and (ii) Network Function Virtualization (NFV) traffic bounds estimation —estimating the total traffic that a data center can handle considering all service requests and their performance constraints. To address these challenges, we propose a model that leverages stochastic network calculus to effectively dimension an NFV DC while ensuring delay and availability bounds for all service requests. Our theoretical model provides a mathematical framework to assess the realization of a single SFC request without delving into the specifics of the realization process. We utilize established availability-aware Virtual Network Function (VNF) placement patterns to obtain traffic bounds essential to planning data center capacity. We analyze NFV data center traffic under various scenarios over a Fat-tree DC topology. The results demonstrate that data center capacity is significantly influenced by the VNF placement strategy. Additionally, for data centers hosting latency-sensitive services, Service Level Objective (SLO) constraints on availability and delay are crucial in determining the number of such requests that can be accommodated.

P4+NFV: Optimal offloading from P4 switches to NFV for diverse traffic streams

Software-defined Networking (SDN) is making its mark in the operator networks. The latest generation of SDN switches supporting paradigms such as P4, are paving the way for complete data plane programmability. Though P4 switches enable some newer applications, they do not provide the same agility and scalability offered through fully programmable softwarized data planes of Virtual Network Functions (VNFs). This paper argues that to achieve substantial performance benefits, an operator can take advantage of deploying P4 switches alongside VNFs. The idea is to use the P4 switch as a default packet handler, whereas traffic can be offloaded to an off-site VNF when queues at the switch start to build. To this end, this paper first models the queuing behavior of a networked system with a P4 switch and a VNF to determine the queuing delay induced by such a hybrid architecture. While doing so, the paper considers two different cases of homogeneous and heterogeneous traffic patterns. Subsequently, the paper proposes algorithms for finding optimal traffic offloading, leading to overall delay minimization. The paper showcases significant performance gains of optimally offloading traffic from a P4 switch to a VNF amidst changes in disparate traffic and network parameters through simulations and analytical results. For instance, at moderate loads of homogeneous traffic, optimal offloading yields performance gains of up to 76.44% over a scenario where a P4 switch handles all the packets. For heterogeneous traffic patterns, the results show that the dominant flow’s workload and average packet size can significantly impact the offloading performance.

Online Service Function Chain Planning for Satellite-Ground Integrated Networks to Minimize End-to-End (E2E) Delay.

The combination of software-defined networking (SDN) and satellite-ground integrated networks (SGINs) is gaining attention as a key infrastructure for meeting the granular quality-of-service (QoS) demands of next-generation mobile communications. However, due to the unpredictable nature of end-user requests and the limited resource capacity of low Earth orbit (LEO) satellites, improper Virtual Network Function (VNF) deployment can lead to significant increases in end-to-end (E2E) delay. To address this challenge, we propose an online algorithm that jointly deploys VNFs and forms routing paths in an event-driven manner in response to end-user requests. The proposed algorithm selectively deploys only the essential VNFs required for each Service Function Chain (SFC), focusing on minimizing E2E delay-a critical QoS parameter. By defining a minimum-hop region (MHR) based on the geographic coordinates of the routing endpoints, we reduce the search space for candidate base stations, thereby designing paths that minimize propagation delays. VNFs are then deployed along these paths to further reduce E2E delay. Simulations demonstrate that the proposed algorithm closely approximates the global optimum, achieving up to 97% similarity in both E2E delay and CPU power consumption, with an average similarity of approximately 90%.

Deep Learning Aided Intelligent Reflective Surfaces for 6G: A Survey

The envisioned sixth-generation (6G) networks anticipate robust support for diverse applications, including massive machine-type communications, ultra-reliable low-latency communications, and enhanced mobile broadband. Intelligent Reflecting surface (IRS) have emerged as a key technology capable of intelligently reconfiguring wireless propagation environments, thereby enhancing overall network performance. Traditional optimization techniques face limitations in meeting the stringent performance requirements of 6G networks due to the intricate and dynamic nature of the wireless environment. Consequently, deep learning (DL) techniques are employed within the IRS framework to optimize wireless system performance. This article provides a comprehensive survey of the latest research in DL-aided IRS models, covering optimal beamforming, resource allocation control, channel estimation and prediction, signal detection, and system deployment. The focus is on presenting promising solutions within the constraints of different hardware configurations. The survey explores challenges, opportunities, and open research issues in DL-aided IRS, considering emerging technologies such as digital twins, computer vision, blockchain, network function virtualization, integrated sensing and communication, software-defined networking, mobile edge computing, unmanned aerial vehicles, and non-orthogonal multiple access. Practical design issues associated with these enabling technologies are also discussed, providing valuable insights into the current state and future directions of this evolving field.