Network Function Virtualization

This document describes a mechanism to control Service Function access to the Network Service Header (NSH). It addresses the Service Function trust issue and provide a method to enforce predefined access control lists to limit Service Function access to Service Function Chain information in the NSH in NSH-based Service Chaining.

One of the major challenges for nano-network is the forfeit of communication protocols to exploit the potential communication between nano-machines forming fully operational nano-network. Because nano-machines face some restrictions such as limited processing power and confined computing capabilities, up-to-date nano-machines cannot perceive partial or full routing tables, which are the main decision-makers for data routing in legacy communication networks. The reason is that creating and updating routing tables continuously require adequate processing power with sufficient memory and computing capabilities, which is not the case of nano-nodes. So, new innovative routing schemes have to be proposed for nano-networks to deal with such extremely low resources. This paper focuses on decoupling the routing intelligence from nano-network towards a computational architecture using Software Defined Networking (SDN) and Network Function Virtualization (NFV) technologies by externalizing routing decisions and complex computations from nano-nodes to be fully compiled externally. Moreover, the paper proposes a probability-based path discovery protocol denoted by (PBPD) for electromagnetic nano-nodes suitable for dynamic nano-network applications. The performance of the proposed protocol is evaluated and compared with other routing protocols discussed in the literature. The proposed scheme provides low energy consumption inside nano-nodes and low computational complexity thanks to SDN/NFV system.

Nano-network is a communication network at the Nano-scale between Nano-devices. Nano-devices face certain challenges in functionalities, because of limitations in their processing capabilities and power management. Hence, these devices are expected to perform simple tasks, which require different and novel approaches. In order to exploit different functionalities of Nano-machines, we need to manage and control a set of Nano-devices in a full Nano-network using an appropriate architecture. This step will enable unrivaled applications in the biomedical, environmental and industrial fields. By the arrival of Internet of Things (IoT) the use of the Internet has transformed, where various types of objects, sensors and devices can interact making our future networks connect nearly everything from traditional network devices to people. In this paper, we provide an unified architectural model of Nano-network communication with a layered approach combining Software Defined Network (SDN), Network Function Virtualization (NFV) and IoT technologies and present how this combination can help in Nano-networks' context. Consequently, we propose a set of functions and use cases that can be implemented by Nanodevices and discuss the significant challenges in implementing these functions with Nano-technology paradigm and the open research issues that need to be addressed.

The pervasive distribution of digital devices (e.g., laptops, mobile phone, digital camera, music players, RFID, sensors, smart cards) will create a large distributed computational and networking environment that will foster the evolution towards ecosystems of resource, services and data. Dynamicity and ubiquity of said future environments pose new challenges and requirements that current infrastructures cannot meet. Several limitations have to be overcomed, for instance: low flexibility and scalability, missing optimized cross-layer/cognitive (and cross-domain) resources allocation, limited potentialities of fostering new business models and above all a brittle integration of IT and network solutions/technologies. This paper proposes a systemic design to develop a future network architecture overcoming said limitations. Architecture is based on three main decentralised planes: the Knowledge Plane (KP), the Network Control Plane (NCP) and the Resource Control Plane (RCP). The three planes are implemented a distributed way and interact with each other (through a signalling overlay) for the allocation and management of virtual (communication, storage and processing) resources in overlay networks: novelty stands in coupling some enabling technologies with the creation and use of a cross-layer network knowledge. Paper also presents, as an example, some simulation results showing the optimal allocation of resources for the proposed architecture. Eventually, recommendations and future work conclude the paper.

The transport & logistics (T&L) sector plays a pivotal role in modern production and distributed systems, significantly impacting people's lives. However, this industry segment faces challenges with low automation and process optimization, directly affecting the efficiency and safety of T&L operations. To address these issues, pilot trials utilizing 5G mobile communication systems, edge cloud, software-defined networking (SDN)/ network function virtualization (NFV), network applications [1, 2], and OpenAPIs are being created in various European initiatives. These trials aim to develop and test various

Virtual Reality (VR) and Augmented Reality (AR) are revolutionizing immersive 360°video experiences across education, entertainment, and cultural exploration. This paper introduces a Smart Video Encoder (SVE) system, comprising server-and user-side Virtual Network Functions (VNFs), for adaptive compression and transmission of 360°video streams over 5G/6G networks. Leveraging a Supervised Machine Learning (SML) Video Analyzer, the system enhances userperceived quality by prioritizing key regions, such as artistic content. Using hierarchical tile-based compression, the SVE adjusts compression dynamically based on real-time user behavior and video content. Results show its effectiveness in maintaining high-quality experiences under bandwidth constraints, particularly for applications like virtual museum tours.

Providing Service Chains consisting of different virtual functions in dynamic and infrastructure-less environments, such as disasterstricken regions or remote rural areas, requires specialized orchestration strategies. A promising approach is to leverage Unmanned Aerial Vehicles (UAVs) organized into Flying Ad-hoc Networks (FANETs) to provide Edge Computing capabilities. These UAVs act as distributed computing nodes, hosting Cloud-Native Functions (CNFs) that process and store data for ground users. However, the constraints of UAVs necessitate optimized resource allocation strategies to balance utility and performance. This paper presents a marketplace approach for Service Chain deployment in a multi-layer FANET Edge Computing architecture. The proposed framework introduces multiple layers, where service requests are aggregated and processed by an Orchestration Layer that optimally distributes application traffic among CNF Providers and Resource Providers. By dynamically allocating computing resources based on demand, this architecture optimizes service performance while minimizing overall costs. The proposed solution contributes to the efficient orchestration of SCs in FANETs, supporting applications in challenging environments.

Advanced communication networks, such as 5G and beyond, will be a complex ecosystem made of multiple physically interconnected elements, implying that the upcoming network will have to address capabilities such as flexibility, programmability and extensibility. This article, describes an Open and Extensible 5G Network Function Virtualisation (NFV) based Reference ecosystem of experimental facilities, named 5GinFIRE, that integrates existing facilities with new vertical-specific ones but also lays down the foundations for instantiation fully softwarised architectures of vertical industries and experimenting with them. Additionally, we present 5GinFIRE as the forerunner experimental playground, together with three uses cases, wherein new components, architecture designs and APIs may be tried and proposed before they are ported to more industrially mainstream 5G networks that are expected to emerge in large scale.

Metropolitan networks are undergoing a major technological breakthrough leveraging the capabilities of softwaredefined networking (SDN) and network function virtualization (NFV). NFV permits the deployment of virtualized network functions (VNFs) on commodity hardware appliances which can be combined with SDN flexibility and programmability of the network infrastructure. SDN/NFV-enabled networks require decision-making in two time scales: short-term online resource allocation and mid-to-long term offline planning. In this paper, we first tackle the dimensioning of SDN/NFV-enabled metropolitan networks paying special attention to the role that latency plays in the capacity planning. We focus on a specific use-case: the metropolitan network that covers the Murcia -Alicante Spanish regions. Then, we propose a latency-aware multilayer service-chain allocation (LA-ML-SCA) algorithm to explore a range of maximum latency requirements and their impact on the resources for dimensioning the metropolitan network. We observe that design costs increase for low latency requirements as more data center facilities need to be spread to get closer to the network edge, reducing the economies of scale on the IT infrastructure. Subsequently, we review our recent joint computation of multi-site VNF placement and multilayer resource allocation in the

In this paper, the problem of temporal isolation among containerized software components running in shared cloud infrastructures is tackled, proposing an approach based on hierarchical real-time CPU scheduling. This allows for reserving a precise share of the available computing power for each container deployed in a multi-core server, so to provide it with a stable performance, independently from the load of other co-located containers. The proposed technique enables the use of reliable modeling techniques for end-to-end service chains that are effective in controlling the application-level performance. An implementation of the technique within the well-known OpenStack cloud orchestration software is presented, focusing on a use-case framed in the context of network function virtualization. The modified OpenStack is capable of leveraging the special real-time scheduling features made available in the underlying Linux operating system through a patch to the in-kernel process scheduler. The effectiveness of the technique is validated by gathering performance data from two applications running in a real test-bed with the mentioned modifications to OpenStack and the Linux kernel. A performance model is developed that tightly models the application behavior under a variety of conditions. Extensive experimentation shows that the proposed mechanism is successful in guaranteeing isolation of individual containerized activities on the platform.

Providing innovative resource-efficient solutions able to mitigate temporal interference among cloud services, concurrently sharing the same underlying platform, is crucial to deploy highly time-sensitive applications at the edge of the network where resources are strongly restrained, and timing constraints are stringent. A notable example is provided by the allocation of virtualized network functions in the radio access network of modern mobile networks, such as 5G. This paper describes a kernel mechanism that can be applied to the design of an architecture providing fine-grain control of the temporal interferences among concurrent real-time services while avoiding overheads related to machine virtualization. On top of them, a model is proposed to meet the required endto-end application performance through tuning of parameters in the underlying novel architecture. We show that theoretical latency/load curves match closely with experimental data gathered from a real implementation carried out using both a networking microbenchmark and a real IMS application.

We consider the problem of dynamic platoon leader selection, user association, channel assignment, and power allocation on a cellular vehicle-to-everything (C-V2X) based highway, where multiple vehicle-to-vehicle (V2V) and vehicle-toinfrastructure (V2I) links share the frequency resources. There are multiple roadside units (RSUs) on a highway, and vehicles can form platoons, which has been identified as an advanced use-case to increase road efficiency. The traditional optimization methods, requiring global channel information at a central controller, are not viable for high-mobility vehicular networks. To deal with this challenge, we propose a distributed multi-agent reinforcement learning (MARL) for resource allocation (RA). Each platoon leader, acting as an agent, can collaborate with other agents for joint sub-band selection and power allocation for its V2V links, and joint user association and power control for its V2I links. Moreover, each platoon can dynamically select the vehicle most suitable to be the platoon leader. We aim to maximize the V2V and V2I packet delivery probability in the desired latency using the deep Q-learning algorithm. Simulation results indicate that our proposed MARL outperforms the centralized hill-climbing algorithm, and platoon leader selection helps to improve both V2V and V2I performance.

Most autonomous vehicle (AV) systems today operate like lone wolves-high-tech, self-reliant, and isolated. A single vehicle collects data, interprets its surroundings, and makes decisions independently. It may use advanced tools like LiDAR, radar, or neural networks, but it ultimately remains a solitary agent in a shared environment. This isn't just a coordination issue; it's a fundamental misinterpretation of what autonomy could be. Valence rethinks this paradigm. Rather than viewing each vehicle as an isolated intelligence, it treats them as interconnected neurons within a larger synthetic brain. Each vehicle shares data and intentions with others, forming a dynamic, distributed cognitive ecosystem. This paper explores the system's architecture, learning framework, philosophical rationale, and the tangible advantages of this collaborative model. The Future of AVs: A Shift That's Already Happening Autonomous vehicles are no longer a distant future-they are an emerging reality driven by urban congestion, sustainability goals, accident reduction, and technological readiness. But as AVs proliferate, the real bottleneck is not perception or motion planning, but interaction. Effective V2V (vehicle-to-vehicle) communication is becoming as vital as steering or braking. Systems like Valence, which support data exchange and shared decision-making, represent the connective tissue for next-generation mobility. Without this collaboration, even the most intelligent vehicles risk becoming dysfunctional in crowded, real-world traffic.

This article examines contemporary approaches to modernizing legacy systems within financial institutions, addressing the critical challenges that outdated technological infrastructure presents to operational efficiency, regulatory compliance, and competitive advantage. The article synthesizes industry best practices across three complementary modernization strategies: microservices transformation, cloud migration, and API-first integration. By exploring the architectural frameworks, implementation methodologies, and organizational considerations associated with these approaches, the article provides financial institutions with a comprehensive roadmap for digital transformation while maintaining operational stability. The article analysis incorporates case studies from leading financial organizations, highlighting both successful implementations and common pitfalls encountered during modernization initiatives. This article contributes to both theoretical understanding and practical application of enterprise architecture principles in financial technology contexts, offering insights into how institutions can balance innovation with the inherent constraints of highly regulated environments. The article suggests that successful modernization requires not only technological reconfiguration but also strategic alignment with business objectives, careful risk management, and organizational culture transformation.

The use of virtualization technologies in high performance computing (HPC) environments has traditionally been avoided due to their inherent performance overhead. However, with the rise of container-based virtualization implementations, such as Linux VServer, OpenVZ and Linux Containers (LXC), it is possible to obtain a very low overhead leading to near native performance. In this work, we conducted a number of experiments in order to perform an in-depth performance evaluation of container-based virtualization for HPC. We also evaluated the trade-off between performance and isolation in container-based virtualization systems and compared them with Xen, which is representative of the traditional hypervisor-based virtualization systems used today.

In this paper we consider a Wireless Mesh Network (WMN) integrating SDN principles. The Wireless Mesh Routers (WMR) are OpenFlow capable switches that can be controlled by SDN controllers, according to the wmSDN (wireless mesh SDN) architecture that we have introduced in a previous work. We consider the issue of controller selection in a scenario with intermittent connectivity. We assume that over time a single WMN can become split in two or more partitions and that separate partitions can merge into a larger one. We assume that a set of SDN controllers can potentially take control of the WMRs. At a given time only one controller should be the master of a WMR and it should be the most appropriate one according to some metric. We argue that the state of the art solutions for "master election" among distributed controllers are not suitable in a mesh networking environment, as they could easily be affected by inconsistencies. We envisage a "master selection" approach which is under the control of each WMR, and guarantees that at a given time only one controller will be master of a WMR. We designed a specific master selection procedure which is very simple in terms of the control logic to be executed in the WMR. We have implemented the proposed solution and deployed it over a network emulator (CORE) and over the combination of two physical wireless testbeds (NITOS and w-iLab.t).

Software Defined Networking (SDN) is a promising architectural approach based on a programmatic separation of the control and data planes. For high availability purposes, logically centralized SDN controllers follow a distributed implementation. While controller role features in the OpenFlow protocol allow switches to communicate with multiple controllers, these mechanisms alone are not sufficient to guarantee a resilient control plane, leaving the actual implementation an open challenge for SDN designers. This paper explores OpenFlow roles for the design of resilient SDN control plane and proposes AR2C2 as an actively replicated multi-controller strategy. As proof of concept, AR2C2 is implemented based on the Ryu controller and relying on OpenReplica to ensure consistent state among the distributed controllers. Our prototype is experimentally evaluated using real commodity switches and Mininet emulated environment. Results of the measured times to recover from failures for different workloads shed some light on the practical trade-offs on replication overhead and latency as a step forward towards SDN resiliency.

This paper presents FlexForward, an approach for dynamically manage SDN data plane forwarding mechanisms. This feature leverages SDN flexibility in two aspects: (i) it provides a new management method, in which topological infrastructure requirements are satisfied by the most appropriate forwarding mechanism, residing in each managed network element; (ii) further decoupling between control and data planes in SDN by offering tableless forwarding support. As proof of concept, an implementation with different forwarding methods is carried out in Open vSwitch. Results show that FlexForward is able to achieve seamless switchover between forwarding methods. It also allows efficient tableless forwarding implementations, improving by up to 31% in latency, and 90% in throughput, when compared to regular OpenFlow.

Service Function Chaining (SFC) is the problem of deploying various network service instances over geographically distributed data centers and providing inter-connectivity among them. The goal is to enable the network traffic to flow smoothly through the underlying network, resulting in an optimal quality of experience to the end-users. Proper chaining of network functions leads to optimal utilization of distributed resources. This has been a de-facto model in the telecom industry with network functions deployed over underlying hardware. Though this model has served the telecom industry well so far, it has been adapted mostly to suit the static behavior of network services and service demands due to the deployment of the services directly over physical resources. This results in network ossification with larger delays to the end-users, especially with the data-centric model in which the computational resources are moving closer to end users. A novel networking paradigm, Network Function Virtualization (NFV), meets the user demands dynamically and reduces operational expenses (OpEx) and capital expenditures (CapEx), by implementing network functions in the software layer known as virtual network functions (VNFs). VNFs are then interconnected to form a complete end-toend service, also known as service function chains (SFCs). In this work, we study the problem of deploying service function chains over network function virtualized architecture. Specifically, we study virtual network function placement problem for the optimal SFC formation across geographically distributed clouds. We set up the problem of minimizing inter-cloud traffic and response time in a multi-cloud scenario as an ILP optimization problem, along with important constraints such as total deployment costs and service level agreements (SLAs). We consider link delays and computational delays in our model. The link queues are modeled as M/D/1 (single server/Poisson arrival/deterministic service times) and server queues as M/M/1 (single server/Poisson arrival/exponential service times) based on the statistical analysis. In addition, we present a novel affinity-based approach (ABA) to solve the problem for larger networks. We provide a performance comparison between the proposed heuristic and simple greedy approach (SGA) used in the state-of-the-art systems. Greedy approach has already been widely studied in the literature for the VM placement problem. Especially we compare our proposed heuristic with a greedy approach using first-fit decreasing (FFD) method. By observing the results, we conclude that the affinity-based approach for placing the service functions in the network produces better results compared against the simple greedy (FFD) approach in terms of both, total delays and total resource cost. We observe that with a little compromise (gap of less than 10% of the optimal) in the solution quality (total delays and cost), affinity-based heuristic can solve the larger problem more quickly than ILP.

Network function virtualization (NFV) over multicloud promises network service providers amazing flexibility in service deployment and optimizing cost. Telecommunications applications are, however, sensitive to performance indicators, especially latency, which tend to get degraded by both the virtualization and the multiple cloud requirement for widely distributed coverage. In this work we propose an efficient framework that uses the novel concept of random cloud selection combined with a support vector regression based predictive model for cost optimized latency aware placement (COLAP) of service function chains. Extensive empirical analysis has been carried out with training datasets generated using a queuingtheoretic model. The results show good generalization performance of the predictive algorithm. The proposed framework can place thousands of virtual network functions in less than a minute and has high acceptance ratio.

Distributed computing systems are well-known to suffer from the problem of slow or failed nodes; these are referred to as stragglers. Straggler mitigation (for distributed matrix computations) has recently been investigated from the standpoint of erasure coding in several works. In this work we present a strategy for distributed matrix-vector multiplication based on convolutional coding. Our scheme can be decoded using a lowcomplexity peeling decoder. The recovery process enjoys excellent numerical stability as compared to Reed-Solomon coding based approaches (which exhibit significant problems owing their badly conditioned decoding matrices). Finally, our schemes are better matched to the practically important case of sparse matrix-vector multiplication as compared to many previous schemes. Extensive simulation results corroborate our findings.

Programmable Network like SDN allows administrators to program network nfrastructure according to service demand and custom-defined policies. Network olicies are interpreted by the centralized controller to define actions and rules to rocess the network traffic on devices that belong to a single domain. However, actual etworks are multi-domain where several domains are interconnected. Then, because DN controllers in a domain cannot define nor monitor policies in other domains, etwork administrators cannot ensure that their own policies, origin policies are being nforced by the domains not directly managed by them (i.e. foreign domains). e present AudiT, a multi-domain SDN policy verifier that identifies whether an rigin policy is enforced by foreign domains. AudiT comprises (1) model for network opology, policies, and flows, (2) an Audit protocol to gather information about the ctions performed by network devices to carry the flows of interest, and (3) a validation ngine that takes ...

The rising number of road accidents worldwide underscores the urgent need to implement Vehicle-to-Everything (V2X) communications, which can help minimize fatalities and injuries. This paper proposes a novel cloud-based architecture with machine learning capabilities that enables smooth, intelligent management of V2X services. The system features a backup controller that can take over in case of failure, ensuring uninterrupted operation. Additionally, the ML models provide invaluable optimization, from predicting traffic patterns to maximizing resource utilization and connection quality. With their independent decision-making, these Artificial Intelligent/ Machine Learning (AI/ML) functions act as a catalyst, enhancing overall efficiency. This research sheds new light on employing AI/ML in integrating non-terrestrial networks, addressing the complex challenges of massive data analytics under strict requirements. By outlining future research directions, it makes a significant contribution to the knowledge base around intelligent V2X systems. The proposed architecture demonstrates how AI/ML can be harnessed to create robust, seamless networks that help protect human lives on the road. Consequently, this paper introduces a novel architecture leveraging Software-Defined Networking (SDN) over the cloud, which decouples the control plane from the data plane, providing enhanced network programmability and scalability while minimizing costs and network congestion. Additionally, the paper presents a Vulnerable Road User (VRU) accident detection system within this architecture. The proposed method be able to analyze the incoming data in real time, identify potential collision risks with more than 90% accuracy, and provide warnings or take preventive actions to mitigate the risk of accidents. This can greatly improve road safety by enabling vehicles to make informed decisions and take appropriate measures to avoid collisions

In the last few years, Virtual Reality (VR) is assuming a prominent role and is recognized as a pivotal technology in various sectors. However, transmission of immersive videos produced by real-time 360° cameras or stored on remote servers to reproduce 3D environments, or streamed by video games accessible through headsets, would require a lot of network bandwidth that, in many cases, is not available or too expensive to be obtained. In this paper, we leverage on network softwarization provided by new 5G&B networks, and introduce an Adaptive Closed-loop Encoding VNF named 360-ST for adaptive compression of 360° video streaming. This VNF is able to apply a hierarchical compression that takes into account both the bandwidth currently available in the network, and the user viewport. The agent that is in charge of deciding the different compression ratio at runtime uses Deep Reinforcement Learning to optimize a reward function and adapt to the changes of the network bandwidth, the end-to-end latency, the user movements within the scene and the video content. The results indicate that our proposed method consistently outperforms state-of-the-art algorithms by an average of 8% to 46% in terms of achieved Peak Signal-to-Noise Ratio (PSNR).

This comprehensive article examines the emerging trends in cloud technologies, focusing on automation and data engineering within modern enterprise architectures. The article explores the evolution from traditional computing models to sophisticated federation frameworks, highlighting the transformative impact across various industry sectors. The investigation encompasses next-generation cloud infrastructure, intelligent automation advances, modern data engineering paradigms, and industry-specific applications. The article analyzes the integration of edge computing, zero-trust security models, AI-driven orchestration, and self-healing systems, demonstrating how these technologies are reshaping operational paradigms. Through detailed examination of real-world implementations and industry case studies, the article illustrates the convergence of cloud computing with advanced automation technologies, emphasizing their role in enhancing operational efficiency, security, and business agility. The findings underscore the critical importance of adaptive technological frameworks in maintaining competitive advantage while addressing emerging challenges in data governance, security, and sustainability.

The advent of next generation wireless networks, particularly 5G and the upcoming 6G technologies, has drastically altered the landscape of data processing and communication systems. One of the key advancements is the integration of edge computing with wireless networks to enhance the performance and efficiency of sensor networks. This paper explores the synergistic approach between edge computing and next generation wireless networks, specifically how this collaboration enables efficient sensor data processing for various Internet of Things (IoT) applications. With an emphasis on low latency communication, real time data processing, and improved scalability, the combined use of edge computing and wireless networks addresses challenges such as network congestion, latency, and power consumption in sensor driven systems. The paper also discusses the application of this integrated framework in sectors like healthcare, autonomous vehicles, and smart cities, while highlighting key challenges, solutions, and future research opportunities.

The rapid evolution of computer networks has spurred innovations in network softwarization, virtualization, and optimization, particularly in Voice over IP (VoIP) systems and Wireless Sensor Networks (WSNs) for smart grids. This article synthesizes four pivotal studies addressing these domains: softwarization and virtualization of VoIP networks, lifetime optimization in WSNs for smart grids, dynamic load balancing in Software-Defined Networking (SDN) controllers, and predictive load optimization in IoT multimedia environments. We propose a novel Hybrid SDN-NFV-Predictive Framework (HSNPF) integrating SDN, Network Function Virtualization (NFV), and Long Short-Term Memory (LSTM)-based predictive analytics to enhance network performance, scalability, and energy efficiency. The proposed mechanism is evaluated through simulations, demonstrating significant improvements in load balancing, energy consumption, and Quality of Service (QoS). Comparative analyses, detailed evaluations, and future research directions are provided to guide advancements in network management.

Background Information: Software-Defined Networking (SDN) and Network Function Virtualization (NFV) allow for flexibility at the cost of new security vulnerabilities. Conventional security measures cannot handle these problems because NFV is dynamic. IDS integrated with SDN brings in enhanced security through centralized management and real-time threat detection, enhanced visibility, risk mitigation, and the ability to keep pace with changing cyber threats in virtualized networks. Objectives: Improvements in scalability and flexibility of network security, real-time threat detection and response, security in dynamic NFV environments, and addressing shortcomings of conventional security models are the goals. Methods: Using Principal Component Analysis (PCA) for feature extraction and Artificial Neural Networks (ANN) for classification, the framework uses a hybrid PCA-ANN model for intrusion detection. SDN-managed adaptive traffic rerouting modifies its course in response to risk scores derived from questionable network activities. Results: In terms of both detection accuracy and response time, the suggested framework outperformed existing techniques such as Wasserstein GAN and Web Application Firewall, achieving a 93% detection accuracy. It proved to be quite successful in dynamic NFV situations, exhibiting reduced false positive (0.05) and false negative (0.03) rates. Conclusion: By providing a scalable, adaptable, and effective means of enhancing security in virtualized network settings, the Hybrid SDN-IDS architecture opens the door for further developments in edge computing and 5G networks.

Background: With an increased need for scalable cloud infrastructures, combining SDN with Open vSwitch (OVS) in virtualized settings presents performance problems. The Autonomous Virtual Environment Cloud (AVEC) framework is proposed to boost scalability, security, and network performance. Methods: The study assesses essential performance indicators such as Throughput, Latency, and Resource usage under different network loads. It compares OVS with and without SDN to the AVEC framework through experimental testing in cloud data centers. Objectives: The study seeks to evaluate the AVEC framework's capacity to improve SDN-OVS performance and scalability in cloud computing environments. Results: Under heavy network loads, the AVEC architecture improved Throughput and lowered Latency significantly, exceeding typical OVS-SDN configurations, particularly in large-scale cloud data center deployments. Conclusion: Integrating the AVEC framework improves network efficiency and scalability in cloud infrastructures, providing a viable way to address the limitations of SDN and OVS in virtualized settings.

In recent years, the convergence of Augmented Intelligence with Internet of Things (IoT) technologies has revolutionized numerous domains, from healthcare to transportation. In the area of vehicular and road cooperation applications, the Augmented Intelligence has been studied for addressing problems with data acquisition, processing, and real-time decisionmaking, particularly in enhancing traffic coordination, vehicle safety, and energy efficiency. Thus, the proposed work explores the integration of Augmented Intelligence with IoT (AIoT) in autonomous vehicles. We present a framework that leverages dynamic network coding and advanced data management strategies, NetCodeAIoT, to enhance the AIoT communication in 5G networks, focusing on applications within the vehicle industry. This pioneering integration of dynamic network coding and deep learning uniquely addresses scalability and security challenges in vehicular AIoT systems. NetCodeAIoT dynamically adjusts the sparsity level of the decoding matrix, implements unequal error protection network coding, and enables instantaneous decoding of data. These techniques optimize transmission efficiency and enhance security in AIoT scenarios, using a deep learning architecture. Experimental evaluations were performed with NetCodeAIoT, demonstrating an improvement in network efficiency and a reduction in average time delay compared to traditional AIoT communication approaches.

This paper explores the feasibility of applying sleep-mode strategies in a realistic RAN network, using a deployment scenario from a local MNO as a case study. By modelling spatially correlated shadowing and 3GPP-compliant path loss, we evaluate individual and combined eNB coverage under both optimized and sequential activation schemes. Results show that optimisation significantly improves coverage at low activation levels, achieving near-complete service with fewer active sites. A fitted similarity model characterizes convergence between the two approaches, introducing a deployment-specific parameter α that helps guide practical energy-saving decisions based on traffic load. The proposed method offers both performance gains and planning insights for dynamic, energy-aware RAN control.

The rapid growth of Internet of Things (IoT) and Voice over IP (VoIP) networks has introduced significant challenges in resource management, including Quality of Service (QoS) assurance, load balancing, and energy efficiency. Two key frameworks addressing these challenges are presented in the attached articles: (1) a Software-Defined Networking (SDN)-based IoT framework for load balancing and QoS-aware routing, and (2) GreenVoIP, an SDN/Network Function Virtualization (NFV)-based framework for energyefficient VoIP resource allocation. This paper provides a detailed comparative analysis of these frameworks, highlighting their methodologies, strengths, weaknesses, and future research directions. We evaluate their performance in real-world testbeds, discuss their scalability, and propose enhancements for future implementations.
Ahmadreza Montazerolghaem

The rapid evolution of 5G and LTE networks, coupled with the increasing diversity of user requirements, has necessitated intelligent and dynamic resource allocation techniques. This paper explores the application of reinforcement learning (RL) algorithms-specifically Deep Q-Network (DQN), Proximal Policy Optimization (PPO), and Mean Field Qlearning (MFQ)-to optimize network resource allocation based on critical Quality of Service (QoS) parameters such as packet loss rate and packet delay. Utilizing a real-world-inspired dataset that includes multiple dimensions like time of day, user equipment categories, and usage scenarios across various sectors (healthcare, public safety, smart cities, etc.), an OpenAI Gymcompatible environment is developed to simulate real-time allocation challenges. Each RL agent learns to allocate network resources efficiently under changing conditions, aiming to minimize packet loss and delay while maximizing system throughput. The paper further includes the deployment of these trained models using Django on a local server, with a user-friendly Reactbased frontend that accepts input features and returns the allocation results from all three models. Comparative analysis of cumulative rewards, convergence rates, and action selections helps determine the effectiveness of each algorithm. The system demonstrates a scalable, intelligent approach to managing the growing demands of next-generation wireless networks, emphasizing practical feasibility and deployment readiness.

The integration of cloud-native artificial intelligence (AI) technologies into predictive maintenance frameworks within the energy sector has emerged as a pivotal paradigm for enhancing operational reliability, optimizing asset performance, and minimizing unplanned downtime. This paper presents a comprehensive analysis of cloud-native AI solutions specifically tailored for predictive maintenance, emphasizing the inherent security implications associated with deploying such architectures in mission-critical energy infrastructures. Through an in-depth exploration of containerized microservices, edge-cloud orchestration, real-time data ingestion pipelines, and AI-driven anomaly detection algorithms, this study underscores the technical sophistication and adaptability of cloud-native approaches. Special attention is given to the security posture of these systems, including vulnerabilities arising from distributed computing, data privacy concerns, threat vectors in multi-tenant cloud environments, and secure model deployment practices. The paper further explores regulatory and compliance considerations in the context of cybersecurity standards for energy systems. The findings highlight the dual imperative of maintaining system integrity while leveraging scalable AI solutions for predictive insights.

A typical healthcare system needs further participation with patient monitoring, vital signs sensors and other medical devices. Healthcare moved from a traditional central hospital to scattered patients. Healthcare systems receive help from emerging technology innovations such as fifth generation (5G) communication infrastructure: internet of things (IoT), machine learning (ML), and artificial intelligence (AI). Healthcare providers benefit from IoT capabilities to comfort patients by using smart appliances that improve the healthcare level they receive. These IoT smart healthcare gadgets produce massive data volume. It is crucial to use very high-speed communication networks such as 5G wireless technology with the increased communication bandwidth, data transmission efficiency and reduced communication delay and latency, thus leading to strengthen the precise requirements of healthcare big data utilities. The adaptation of 5G in smart healthcare networks allows increasing number of...

Network Virtualization (NV) techniques enable high scalability and isolation by abstracting physical resources to provide a logical network representation that can coexist with a physical networking framework. Traditional NV is prone to security attacks and has lower privacy and trustfulness compared to blockchain-established NV. We diagnose the BC-established NV construct under 5 segments and closely appraise the literature in reference to NV technique, virtualization technology, BC-related properties, and network properties. We racked up a starting sample of 85 sources by filtering literary work for qualifying conditions searched from article retrieval platforms, engaging a rigorous and prolonged approach. Anchored from this research, in BC-established NV, we demonstrate that BC can act as a broker/manager for NV, act as a secure storage by preventing double-spending attacks, provide secure virtual network embedding with high fault tolerance, engage BC and smart contacts for resource trading in the process of NV, engage dedicated consensus approaches to reach agreement for NV among multiple parties for reducing security attacks, and establish BC-established access control for NV. Complete interpretation disseminates that from interpreted BCestablished NV schemes, 45% engage BC and smart contracts for agreements and resource trading for NV, 95% engage regular BC architecture, Proof-of-Work (PoW) and Practical Byzantine Fault Tolerance (PBFT) being the most frequently used consensus, 80% engage the overlay network concept, and it has been engaged abundantly (27.5%) in 5G networks. Finally, we deliberate the possibilities and obstacles of the framework of blockchain-established NV and then provide suggestions to suppress them.

In the context of the Open Network Automation Platform (ONAP), we develop in this paper a resource allocation strategy for deploying Virtualized Network Functions (VNFs) on distributed data centers. For this purpose, we rely on a three-level data center hierarchy exploiting co-location facilities available within Main and Core Central Offices. We precisely propose an active VNFs' placement strategy, which dynamically offload requests on the basis of the load observed within a data center. We compare via simulations the performance of the proposed solution against mechanisms so far proposed in the literature, notably the centralized approach of the multi-site project within OpenStack, currently adopted by ONAP. It turns out that the proposed algorithm yields better performance in terms of both data center occupancy and overhead. Furthermore, it allows extending the applicability of ONAP in the context of distributed cloud, without requiring any modification.

How does a fantasy app handle large user traffic during peak seasons? Fantasy sports have become a global phenomenon, attracting millions of users, especially during major tournaments like the IPL, ICC World Cup, and national leagues. However, handling high user traffic during peak seasons is one of the biggest challenges for any fantasy sports platform. A Fantasy Sports App Development Company must implement advanced technologies and strategies to ensure a seamless experience for users, even during high-demand periods. With fantasy sports app development services in the USA, businesses can build high-performance, scalable, and reliable platforms that maintain speed, efficiency, and security even when millions of users are online simultaneously. In this article, we explore how fantasy sports app development companies manage large user traffic effectively.

Software Defined Networks (SDN) is an emerging new network paradigm which enables network programmability and breaks the network vertical integration by separating network intelligence from underlying network devices such as routers and switches. SDN promotes the logically centralized control to program the network. SDN decouples data plane and control plane of the network devices to simplify the network management and great innovation by network programmability, using OpenFlow as a communication protocol between SDN controller and network elements. This paper presents a comprehensive critical survey on SDN and OpenFlow. The main aim of this paper is to give a brief introduction of SDN, the basic architecture of SDN and to show the control plane and data plane separation. The building blocks of SDN as layers are provided with study of infrastructure, southbound, controllers, northbound and network applications. Later research challenges and distributed computing in SDN are discussed to provide future researcher's brief idea about the future scope in the field.

The adoption of Artificial Intelligence (AI) is helping in becoming the transformative force in cloud cost optimization as it is using its capability of predicting analytics, automation and intelligent decision making. Historical as well real time data fed into AI driven solutions can also predict future usage of cloud resources with high accuracy, budgeting, dynamic resource allocation and most importantly cost reduction. AI helps in understanding the unusual spending pattern and prevents cost overruns through implementing anomaly detection mechanisms. ML based automated resource optimization makes sure that the not only cloud works loads are right sized but also minimize underutilization and over-provisioning. FinOps AI, cloud-native AIOps, reinforcement learning based optimization models are new technologies that make resource usage more efficient as these technologies are capable of continuous learning of the behaviour of a workload and act of adjusting resources as per the demand. Business are able to shift workloads across multi cloud environments based on its cost performance trade off using auto scaling using AI and intelligence workload placement. Unlike other tools on the market, these tools integrate AI models in the tool to offer real time cost insights and therefore cost optimization recommendations such as AWS Compute Optimizer, Google Cloud Recommender, and Azure Cost Management to name a few.

Tensor operations, such as matrix multiplication, are central to large-scale machine learning applications. For user-driven tasks these operations can be carried out on a distributed computing platform with a master server at the user side and multiple workers in the cloud operating in parallel. For distributed platforms, it has been recently shown that coding over the input data matrices can reduce the computational delay, yielding a trade-off between recovery threshold and communication load. In this paper we impose an additional security constraint on the data matrices and assume that workers can collude to eavesdrop on the content of these data matrices. Specifically, we introduce a novel class of secure codes, referred to as secure generalized PolyDot codes, that generalizes previously published non-secure versions of these codes for matrix multiplication. These codes extend the state-of-the-art by allowing a flexible trade-off between recovery threshold and communication load for a fixed maximum number of colluding workers.

Data and analytics capabilities have made a leap forward in recent years. The volume of available data has grown exponentially. The huge amount of data needs to be transferred and stored with extremely high reliability. The concept of coded computing , or a distributed computing paradigm that utilizes coding theory to smartly inject and leverage data/computation redundancy into distributed computing systems, mitigates the fundamental performance bottlenecks for running large-scale data analytics. In this dissertation, a distributed computing framework, first for input files distributedly stored on the uplink of a cloud radio access network architecture, is studied. It focuses on that decoding at the cloud takes place via network function virtualization on commercial off-the-shelf servers. In order to mitigate the impact of straggling decoders in this platform, a novel coding strategy is proposed, whereby the cloud re-encodes the received frames via a linear code before distributing ...

Large matrix multiplications are central to largescale machine learning applications. These operations are often carried out on a distributed computing platform with a master server and multiple workers in the cloud operating in parallel. For such distributed platforms, it has been recently shown that coding over the input data matrices can reduce the computational delay, yielding a trade-off between recovery threshold, i.e., the number of workers required to recover the matrix product, and communication load, i.e., the total amount of data to be downloaded from the workers. In this paper, in addition to exact recovery requirements, we impose security and privacy constraints on the data matrices, and study the recovery threshold as a function of the communication load. We first assume that both matrices contain private information and that workers can collude to eavesdrop on the content of these data matrices. For this problem, we introduce a novel class of secure codes, referred to as secure generalized PolyDot (SGPD) codes, that generalize state-of-the-art non-secure codes for matrix multiplication. SGPD codes allow a flexible trade-off between recovery threshold and communication load for a fixed maximum number of colluding workers while providing perfect secrecy for the two data matrices. We then study a connection between secure matrix multiplication and private information retrieval. We specifically assume that one of the data matrices is taken from a public set known to all the workers. In this setup, the identity of the matrix of interest should be kept private from the workers. For this model, we present a variant of generalized PolyDot codes that can guarantee both secrecy of one matrix and privacy for the identity of the other matrix for the case of no colluding servers.

The uplink of a cloud radio access network architecture is studied in which decoding at the cloud takes place via network function virtualization on commercial off-the-shelf servers. In order to mitigate the impact of straggling decoders in this platform, a novel coding strategy is proposed, whereby the cloud re-encodes the received frames via a linear code before distributing them to the decoding processors. Transmission of a single frame is considered first, and upper bounds on the resulting frame unavailability probability as a function of the decoding latency are derived by assuming a binary symmetric channel for uplink communications. Then, the analysis is extended to account for random frame arrival times. In this case, the tradeoff between average decoding latency and the frame error rate is studied for two different queuing policies, whereby the servers carry out per-frame decoding or continuous decoding, respectively. Numerical examples demonstrate that the bounds are useful tools for code design and that coding is instrumental in obtaining a desirable compromise between decoding latency and reliability.

This article comprehensively analyzes edge computing implementation strategies for optimizing API performance in cruise ship environments. The article explores how edge computing technologies can enhance the scalability and performance of cloudbased APIs in maritime settings while incorporating agile development principles. The article examines the unique challenges of shipboard connectivity and presents solutions through distributed computing architectures. The article demonstrates how edge computing can significantly reduce latency, optimize bandwidth usage, and improve overall service quality for passengers and crew members. The article also addresses the technical considerations of implementing edge computing in maritime environments, including hardware requirements, environmental factors, and integration with existing ship systems. The findings reveal that edge computing, when properly implemented with Optimizing Cruise Ship API Performance Through Edge Computing: An Agile Implementation Strategy

Road safety is a global priority, with aggressive driving behaviors posing serious risks to drivers and communities. This paper introduces a blockchain driven framework integrated with Internet of Things (IoT) technologies for rewarding safe driving practices through a novel earning method, focusing on cooperative platooning scenarios. The proposed model assesses driver behavior daily, assigning ranks and rewarding cryptocurrency tokens based on driving performance. Special emphasis is placed on the roles within a platoon, with leaders-who manage communication and maneuvers-earning higher rewards than followers. This incentivization mechanism is securely implemented using blockchain technology, allowing for transparent transactions and fair distribution of rewards. The framework's effectiveness is tested through simulations, highlighting the potential of a token-based reward system leveraging IoT data to promote responsible driving and improve road safety within cooperative platooning setups.

Capacitated Controller Locating Problem (CCLP) finds its application in Software Defined Networks (SDN), for locating controllers. The problem of locating a minimum number of controllers in a weighted topology such that each controller should have a maximum number of switches within the given capacity and reduces interactive time with its switches is known as capacitated controller locating problem. This paper proposes an efficient heuristic approach to solve CCLP.

The rapid evolution of network architectures has increased the demand for scalable, secure, and automated solutions to manage complex multi-cloud environments. VMware NSX-T Data Center is a leading software-defined networking (SDN) platform that offers enhanced network virtualization, micro-segmentation, and Zero Trust security frameworks. This paper presents a comprehensive analysis of NSX-T's transformative impact on modern networks. Results show that NSX-T reduces lateral attack surfaces by 95%, improves cloud resource provisioning times by 50%, and lowers manual configuration efforts by 60%. Additionally, latency reductions of 50% in 5G networks and throughput increases of 50% in large-scale data centers highlight its performance benefits. Despite deployment complexities and cost challenges, NSX-T demonstrates significant potential in advancing 5G, edge computing, and AI-driven network automation.

In modern life the internet has become backbone of digital society which is the packet switched distributed network and connected with almost every digital devices, available everywhere in the world. The traditional network carries few challenges like, dependency on vendors, difficulty to manage large network, dynamically changing of forwarding policies and more. To overcome such challenges, today the idea of Software Defined Networking (SDN) came into existence. The basic idea behind SDN is to implement programmable network by separately managing the control plane and data plane to improve the efficiency of network performance. The main problem with SDN is the Controller Placement Problem (CPP), which gives the overview of whole network. Today the main focus of the researchers is to solve the CPP. CPP is a NP-hard problem because the network should consist of minimum controllers and controllers should be placed on appropriate locations. For the large size network, controller deployment is difficult to manage. But the challenge in this area is Quality of Services (QoS) in respect of controller management. This paper investigates the systematic review of QoS based on controller's problems, analyzed the current research and summarized the findings of the different controller's performance based on QoS parameters e.g. reliability, scalability, consistency and load balancing. Finally, this paper also highlights the research challenges to improve the QoS in SDN.