Search Results (28)

The space–air–ground integrated network (SAGIN) enhanced by mobile edge computing (MEC) has emerged as a promising architecture for future 6G systems, providing wide-area coverage and distributed computing capabilities. By representing requests as service function chains (SFCs), network function virtualization (NFV) enables coordinated orchestration of underlying resources. However, SFC orchestration in SAGIN-MEC faces three significant challenges, including multi-layer resource heterogeneity, topology dynamics, and complex sequential dependencies within SFCs. To address these challenges, this paper proposes GT-PPO, a deep reinforcement learning (DRL)-based approach for online SFC orchestration designed to maximize network profit while minimizing end-to-end (E2E) delay. GT-PPO employs a graph attention network (GAT) to identify interactions among heterogeneous nodes and extract rich feature information from the dynamic physical network. Additionally, it leverages the Transformer self-attention mechanism to encode the SFC context based on resource demands and current deployment progress, thereby capturing global dependencies among virtual network functions (VNFs). Extensive simulation results demonstrate that, under high-load conditions, GT-PPO outperforms representative baselines, increasing the request acceptance ratio and network profit by 5.62% and 16.71%, respectively, while reducing the average E2E delay by 15.46%. Full article

Network functions virtualization (NFV) is an emerging technology that enables flexible service deployment for supporting the Beyond 5G/6G network. NFV transforms physical network devices into virtual network functions (VNF) over Edge Computing capabilities, thereby facilitating the agility of network services and reducing management costs. To effectively monitor Internet of Things (IoT) network resources, service function chaining (SFC) is used for its virtualizations to ensure the multi-service requirements are sufficiently in capability, scalability, and flexibility for computation workloads alignments. However, to satisfy the resource availability requirements and efficiency under several conditions, SFC reconfiguration methods face the challenges in meeting significant latency requirement of delay-sensitive applications while reaching the importance of energy saving on orchestration timespan. In this paper, we propose task management-aware SFC and orchestrating schemes, namely GNN-PPO. In this framework, we utilize the Graph Neural Network (GNN), which relies on the message-passing neural network (MPNN), to capture all the abstraction of physical resource nodes and link capabilities over MEC node states. In particularly, GNN is divided construction into two phrases: (1) GNN represents nodes for all the Mobile edge computing (MEC) nodes, which have a global view on resources of computation and communicational capabilities that could serve as carriers; (2) VNFs are transferred into graph networks by using feature-extraction MPNN to manage each VIM that seeks an optimal and reliable analysis of traffic fluctuations. Lastly, Deep Reinforcement Learning (DRL) is used to embrace the network determination in policy strategy, which utilizes a Proximal Policy Gradient (PPO). On the other hand, we propose a novel network architecture based on PPO to perform the design for the optimization of resource utilization and facilitate energy consumption on MEC servers under diverse setting scenarios, which enables continuous policy enforcement for our system. With the experimental results, we compare our proposed solution with reference schemes in terms of rewards with learning rate and batch size, average request acceptance, SFC success, packet delivery, throughput, and resource utilization ratio that confirm the scheme’s scalability and practical suitability for IoT network deployment. Full article

This paper provides a comprehensive overview of mobile network softwarization, emphasizing the technological foundations and its transformative impact on the energy efficiency of modern and future mobile networks. In the paper, a detailed analysis of communication concepts known as software-defined networking (SDN) and network function virtualization (NFV) is presented, with a description of their architectural principles, operational mechanisms, and the associated interfaces and management frameworks that enable programmability, virtualization, and centralized control in modern mobile networks. The study further explores the role of cloud computing, virtualization platforms, distributed SDN controllers, and resource orchestration systems, outlining how they collectively support mobile network scalability, automation, and service agility. To assess the maturity and evolution of mobile network softwarization, the paper reviews contemporary research directions, including SDN security, machine-learning-assisted traffic management, dynamic service function chaining, virtual network function (VNF) placement and migration, blockchain-based trust mechanisms, and artificial intelligence (AI)-enabled self-optimization. The analysis also evaluates the relationship between mobile network softwarization and energy consumption, presenting the main SDN- and NFV-based techniques that contribute to reducing mobile network power usage, such as traffic-aware control, rule placement optimization, end-host-aware strategies, VNF consolidation, and dynamic resource scaling. Findings indicate that although fifth-generation (5G) mobile network standalone deployments capable of fully exploiting softwarization remain limited, softwarized SDN/NFV-based architectures provide measurable benefits in reducing network operational costs and improving energy efficiency, especially when combined with AI-driven automation. The paper concludes that mobile network softwarization represents an essential enabler for sustainable 5G and future beyond-5G systems, while highlighting the need for continued research into scalable automation, interoperable architectures, and energy-efficient softwarized network designs. Full article

The advent of sixth-generation (6G) communications envisions a paradigm of ubiquitous intelligence and seamless physical–digital fusion, demanding unprecedented performance from the optical transport infrastructure. Achieving terabit-per-second capacities, microsecond latency, and nanosecond synchronisation precision requires a convergent, flexible, open, and AI-native x-Haul architecture that integrates communication with distributed edge computing. This study conducts a systematic literature review of recent advances, challenges, and enabling optical technologies for intelligent and autonomous 6G networks. Using the PRISMA methodology, it analyses sources from IEEE, ACM, and major international conferences, complemented by standards from ITU-T, 3GPP, and O-RAN. The review examines key optical domains including Coherent PON (CPON), Spatial Division Multiplexing (SDM), Hollow-Core Fibre (HCF), Free-Space Optics (FSO), Photonic Integrated Circuits (PICs), and reconfigurable optical switching, together with intelligent management driven by SDN, NFV, and Artificial Intelligence/Machine Learning (AI/ML). The findings reveal that achieving 6G transport targets will require synergistic integration of multiple optical technologies, AI-based orchestration, and nanosecond-level synchronisation through Precision Time Protocol (PTP) over fibre. However, challenges persist regarding scalability, cost, energy efficiency, and global standardisation. Overcoming these barriers will demand strategic R&D investment, open and programmable architectures, early AI-native integration, and sustainability-oriented network design to make optical fibre a key enabler of the intelligent and autonomous 6G ecosystem. Full article

Ensuring ultra-low latency and high reliability in 6G network slices remains a significant challenge, as current NFV orchestration approaches are largely reactive and not designed to anticipate performance degradation. The advent of 6G networks brings forth stringent requirements for ultra-reliable low-latency communication (URLLC), necessitating advanced orchestration mechanisms that go beyond reactive policies in traditional NFV environments. In this paper, we propose a latency-aware, AI-driven NFV slice orchestration framework aligned with ETSI MANO architecture to address the needs of time-sensitive 6G applications. Our framework integrates a predictive AI engine into the NFV Orchestrator (NFVO) to forecast latency violations based on real-time telemetry and historical trends. It enables dynamic scaling, intelligent VNF migration, and infrastructure-level isolation to maintain stringent end-to-end (E2E) latency targets. Experimental results indicate up to 30% reduction in average latency, a 42% improvement in SLA compliance, and 25% lower migration overhead compared to traditional reactive orchestration. The framework provides a scalable and intelligent orchestration solution adaptable to future 6G deployments. Full article

Robust, reliable, and secure communications are essential for efficient railway operation and keeping employees and passengers safe. The Future Railway Mobile Communication System (FRMCS) is a global standard aimed at providing innovative, essential, and high-performance communication applications in railway transport. In comparison with the legacy communication system (GSM-R), it provides high data rates, ultra-high reliability, and low latency. The FRMCS architecture will also benefit from cloud computing, following the principles of the cloud-native 5G core network design based on Network Function Virtualization (NFV). In this paper, an approach to the management of virtualized FRMCS applications is presented. First, the key management functionality related to the virtualized FRMCS application is identified based on an analysis of the different use cases. Next, this functionality is synthesized as RESTful services. The communication between application management and the services is designed as Application Programing Interfaces (APIs). The APIs are formally verified by modeling the management states of an FRMCS application instance from different points of view, and it is mathematically proved that the management state models are synchronized in time. The latency introduced by the designed APIs, as a key performance indicator, is evaluated through emulation. Full article

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

The sharing of mobile network infrastructure has become a key topic with the introduction of 5G due to the high costs of deploying such infrastructures, with neutral host models coupled with features such as network function virtualization (NFV) and network slicing emerging as viable solutions for the challenges in this area. With this in mind, this work presents the design, implementation, and test of a flexible infrastructure-sharing 5G network architecture capable of providing services to any type of client, whether an operator or not. The proposed architecture leverages 5G’s network slicing for traffic isolation and compliance with the policies of different clients, with roaming employed for the authentication of users of operator clients. The proposed architecture was implemented and tested in a simulation environment using the UERANSIM and Open5GS open-source tools. Qualitative tests successfully validated the authentication and the traffic isolation features provided by the slices for the two types of clients. Results also demonstrate that the proposed architecture has a positive impact on the performance of the neutral host network infrastructure, achieving 61.8%-higher throughput and 96.8%-lower packet loss ratio (PLR) in a scenario sharing the infrastructure among four clients and eight users when compared to a single client with all the network resources. Full article

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%. Full article

Vehicular cloud computing (VCC) and connected vehicles have prompted the intensive investigation of communication and computing solutions. As an important enabler, software-defined network (SDN) broadly changes the design of vehicle services, from resource allocation to ambitious autonomous cars. However, current VCC architectures face challenges that hinder the vision of providing reliable services to connected vehicles. As a result, deploying VC services using SDN network has emerged as a viable option. Therefore, software-defined VC architecture (SDVC) dynamically manages the control and resource utilization of VC by centralizing the overall knowledge. In addition, SDN stands as the representative technique of virtual resources and network function virtualization (NFV). NFV is integrated into SDVC frameworks to design extended SDVC (ESDVC) for dynamic, adaptive VC maintenance, VC network slicing management, and to meet constraint requirements such as network latency and reliable connectivity. This paper presents and discusses: (1) the architecture scenario of both SDVC and ESDVC; (2) the effective deployment methods enabling NFV and network slicing (NS) frameworks to customize VC frameworks; (3) challenges and future concepts of more VC services based on ESDVC architecture. From this survey, we believe readers would find relevant methods for realigning information dispersed across the SDVC, fifth generation (5G)-based VC, and NS domains and comprehending the relationships between these technologies while encouraging further debate on the fusion of 5G enabling technologies over SDVC to enable VC network slicing. Full article

Undoubtedly, we are witnessing a new era of computer networks that aspire to support modern demanding applications by providing the highest Quality of Experience (QoE) to the end user. Next Generations Networks (NGNs) ensure that characteristics such as ultra-low latency, high availability and wide service coverage can be met across the network regardless of the network infrastructure ownership. To accomplish that, beyond the necessary improvements in the radio propagation field, changes have been made in the core network functions which are now characterized as programmable, and software defined. Software Defined Networks (SDNs) and Network Function Virtualization (NFV) are the keystones of the NGNs flexibility. The high expectations of NGNs’ performance and the continuous changes in the network conditions lead to the development of new network management frameworks that add elasticity and dynamicity and minimize human intervention. ETSI (the European Standards Organization) presents the Zero-touch Service Management (ZSM) framework that uses hyped technologies such as Artificial Intelligence (AI) and Machine Learning (ML) to achieve full end-to-end automation of the network services’ management across one or many different domains. Focusing on multi-domain network service management, there are several security issues identified by the standardization team which mostly derive from the lack of trust among network providers. In the present research, we explore the suitability of blockchain technology adoption for facing these security issues. Blockchain technology inherently addresses security in trustless environments such as the infrastructures defined by the ZSM team. Our contribution is three-fold: (a) we define the architecture of a multi-domain network infrastructure that adopts the ZSM approach and integrates blockchain functionality, (b) we explore the adoption of different blockchain and distributed ledger technologies (DLT) approaches to address ZSM security needs and (c) we provide guidelines to prospective solution designers/implementers on the detailed requirements that this solution has to meet to maximize the offered value. Full article

Digitalization is one of the factors that affects the acceleration of the application of telecommunications technologies such as 5G. The 5G technology that has been developed today does not yet meet different performance and manageability standards, particularly for data center networks as a supportive technology. Software-defined networking (SDN) and network function virtualization (NFV) are two complementary technologies that are currently used by almost all data centers in the telecommunications industry to rectify performance and manageability issues. In this study, we deliver an integrated SDN-NFV architecture to simplify network management activities in telecommunication companies. To improve network performance at the computing level, we performed a modification of a networking system at the computing level, underlying NFV devices by replacing the default virtual switch with a data plane development kit (DPDK) and single root I/O virtualization (SR-IOV). This study evaluated the proposed architecture design in terms of network performance and manageability. Based on 30 days of observation in prime time, the proposed solution increased throughput up to 200 Mbps for the server leaf and 1.6 Gbps for the border leaf compared to the legacy architecture. Meanwhile, the latency decreased to 12 ms for the server leaf and 17 ms for the border leaf. For manageability, we tested three different scenarios and achieved savings of 13 min for Scenario 1, 22 min for Scenario 2 and 9 min for Scenario 3. Full article

Network Function Virtualization (NFV) offers an alternate method to design, deploy and manage network services. The NFV decouples network functions from the dedicated hardware and moves them to the virtual servers so that they can run in the software. One of the major strengths of the NFV is its ability to dynamically extend or reduce resources allocated to Virtual Network Functions (VNF) as needed and at run-time. There is a need for a comprehensive metering component in the cloud to store and process the metrics/samples for efficient auto-scaling or load-management of the VNF. In this paper, we propose an integrating framework for efficient auto-scaling of VNF using Gnocchi; a time-series database that is integrated within the framework to store, handle and index the time-series data. The objective of this study is to validate the efficacy of employing Gnocchi for auto-scaling of VNF, in terms of aggregated data points, database size, data recovery speed, and memory consumption. The employed methodology is to perform a detailed empirical analysis of the proposed framework by deploying a fully functional cloud to implement NFV architecture using several OpenStack components including Gnocchi. Our results show a significant improvement over the legacy Ceilometer configuration in terms of lower metering storage size, less memory utilization in processing and management of metrics, and reduced time delay in retrieving the monitoring data to evaluate alarms for the auto-scaling of VNF. Full article

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

Network function virtualization (NFV) enables network operators to save costs and flexibility by replacing dedicated hardware with software network functions running on commodity servers. There is a high need for network acceleration to achieve performance comparable to hardware, which is vital for the implementation of NFV. The necessity of NFV acceleration stems from the lengthy packet delivery path following virtualization and the unavailability of generic operating system designs to serve network-specific scenarios. Therefore, the software approach alters the operating system’s processing architecture through Kernel Bypass or offload packet processing to hardware. A typical classification scheme divides it into two main categories based on technology with software and hardware. Only these two categories can be utilized to rapidly and easily establish a classification system. However, it is difficult to suggest the specifics and peculiarities of any acceleration approach during real-world operation. For a more comprehensive classification of NFV acceleration, we refer to the ETSI NFV architectural framework in this research. As the framework clearly illustrates, the technical infrastructure layer of NFV and the corresponding management roles provides a comprehensive and intuitive view of the differences between these acceleration technologies, solutions, and initiatives. Additionally, we conducted an analysis to identify opportunities for improvement in existing solutions and propose new research programs. We expect that NFV will increasingly rely on cloud services in the future. Since cloud services do not offer a choice of hardware, our acceleration method will be primarily software-based. Full article