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作者： Atoui, Wassim Sellil Assy, Nour Gaaloul, Walid Ben Yahia, Imen Grida SAMOVAR Telecom SudParis Paris France Computer Science Department Lebanese University Beirut Lebanon Orange Labs Paris France

Service providers in network function virtualization usually design manually or with static automation the deployment descriptors for virtual network functions. The descriptors are semi-structured files that contain information about the resource requirements and the operational behavior of virtual network functions. Designing the descriptors manually and without formal strategies is certainly a cumbersome and error-prone task for service providers. In this work, we propose a model-driven approach that assists service providers in designing the deployment descriptors. This approach uses a configurable model iteratively to give service providers insights on which best configuration to choose. Concretely, we propose (1) to use a configurable deployment descriptor model, (2) a learning approach based on machine learning to automatically construct the configurable model, and (3) an approach that learns configuration guidelines from a catalog of deployment descriptors to assist service providers with the selection of the configuration to use. The configurable deployment descriptor model captures the relation and also the variability between the virtualized network function (VNF) elements from different deployment descriptors. We propose a learning approach to build the configurable deployment descriptor model by finding and federating similar VNF elements from different deployment descriptors. With our machine learning approach, we construct automatically the configurable model from a set of deployment descriptors. We use afterward the configurable model to learn configuration guidelines from the deployment descriptors and recommend them for service providers. The results of our experiments highlight the effectiveness of our approach to learning configurable deployment descriptor models. © 2021 John Wiley & Sons, Ltd.

关键词： network function virtualization

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Stochastic network Calculus for network function virtualization in Underwater Wireless Sensor networks 1

Stochastic Network Calculus for Network Function Virtualizat...

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1st International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2022

作者： Ponraj, T.C. Subash Sukumaran, Rajeev Vignesh, S.R. Saravanan, M. Manikandan, T.T. Radhakrishnan, M. Srm Institute of Science and Technology Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Mathematics Chennai India

ISBN: (纸本)9781665436472

Underwater Wireless Sensor networks is a popular sub domain of WSN. The Number of Ocean monitoring applications is increasing day-by-day. For effective operations of such underwater applications service provisioning plays a major role. network function virtualization (NFV) has been considered as an effective technology to make flexible service provisioning in terrestrial network. Since UWSN also demands such flexible service provisioning capability, NFV can be adapted for UWSN as well. By providing network operations through NFV in UWSN operational consumptions and capital expenses will be reduced drastically. So, this research focuses on modeling end-end performance bounds on NFV based UWSN using Stochastic network Calculus (SN C). Monitoring applications in UWSN expects on demand service provisioning capability from the underlying network. In order to model such on demand service provisioning features and capability of NFV, we have taken into account both the non-bursty and bursty type of traffic of UWSN. In UWSN for modeling estimation of current resource availability of Virtual network function nodes with multi-level traffic and their complicated NFV chain, using leftover service property and convolution associativity property of SNC has been used. The proposed mathematical model of NFV service provisioning in UWSN has been evaluated for its correctness using a simulation model. The results of the simulation model and the proposed analytical model have a very negligible difference. So, the proposed model can be adapted in real time of effective NFV based service provisioning in UWSN. © 2022 IEEE.

关键词： network function virtualization

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Towards Intent-based Configuration for network function virtualization using In-context Learning in Large Language Models

Towards Intent-based Configuration for Network Function Virt...

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IEEE Symposium on network Operations and Management

作者： Nguyen Van Tu Jae-Hyoung Yoo James Won-Ki Hong Dept. of CSE POSTECH Pohang Korea

ISBN: (数字)9798350327939

ISBN: (纸本)9798350327946

network function virtualization (NFV) enables the execution of Virtual network functions (VNFs) on standard commodity servers. This brings flexibility, allowing for the rapid deployment of various network services while reducing costs. However, NFV configurations are becoming increasingly complex, necessitating experts for the setup. Intent-based network configuration has emerged as a solution to simplify NFV configuration and management. Nonetheless, it presents challenges, such as translating high-level natural language intents into low-level network configurations. In this work, we propose NFV-Intent - a system that leverages in-context learning in Large Language Models to perform the intent translation task. In-context learning enables NFV-Intent to work without retraining the Large Language Models, which is a difficult and expensive task. NFV-Intent uses a JSON template as the desired output, allowing Large Language Models to learn with a small number of examples and enabling easy verification of the configuration. Our evaluation showed that the intent can be translated into JSON configuration with high accuracy. To demonstrate the feasibility of NFV-Intent, we implemented and integrated it into the NI-testbed, our previously developed system for AI-based NFV life-cycle management.

关键词： Costs Accuracy Scalability Natural languages Prototypes Configuration management network function virtualization

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A Two-stage Reconfiguration in network function virtualization: Toward Service function Chain Optimization

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IEEE Transactions on network and Service Management 2025年

作者： Assis, Karcius D. R. Almeida, Raul C. Baghban, Hojjat Santos, Alex F. Boutaba, Raouf Electrical and Computer Engineering Department Salvador Brazil University of Waterloo David R. Cheriton School of Computer Science Canada Department of Electronics and Systems Recife Brazil Chang Gung University Department of Artificial Intelligence Taiwan Chang Gung University College of Intelligent Computing Taiwan Center of Science and Technology in Energy and Sustainability Brazil Postgraduate Program in Computer Science Bahia Brazil

network function virtualization (NFV), as a promising paradigm, speeds up the service deployment by separating network functions from proprietary devices and deploying them on common servers in the form of software. Any service in NFV-enabled networks is achieved as a Service function Chain (SFC) which consists of a series of ordered Virtual network functions (VNFs). However, migration of VNFs for more flexible services within a dynamic NFV-enabled network is a key challenge to be addressed. Current VNF migration studies mainly focus on single VNF migration decisions without considering the sharing and concurrent migration of VNF instances. In this paper, we assume that each deployed VNF is used by multiple SFCs and deal with the optimal placement for the contemporaneous migration of VNFs based on the actual network situation. We formalize the VNF migration and SFC reconfiguration problem as a mathematical model, which aims to minimize the VNF migration between nodes or the total number of core changes per node. The approach is a two-stage MILP based on optimal order to solve the reconfiguration. Extensive evaluation shows that the proposed approach can reduce the change in terms of location or number of cores per node in a 6-node and 14-node networks while ensuring network latency compared with the model without reconfiguration. © 2004-2012 IEEE.

关键词： network function virtualization

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Enhancing network function virtualization and Service function Chaining: Innovative Optimization Strategies and Their Impact

Enhancing Network Function Virtualization and Service Functi...

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Pioneering Developments in Computer Science & Digital Technologies (IC2SDT), International Conference on

作者： Deepak Upadhyay Mridul Gupta Kunj Bihari Sharma Abhay Upadhyay Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798350365016

ISBN: (纸本)9798350365023

Throughout this paper, the reader was able to better understand how innovative optimization techniques through NFV and SFC frameworks could and should be applied to address the developing and shifting focus of contemporary network infrastructures. Focusing on performance metrics performance via latency, throughput, resource utilization, and operational cost, the presented algorithms and machine learning models developed and executed due to this research showed innovative and dynamic optimization of network functions. Through further extensive simulations equalling 125 seconds, our findings reported creative results of improving performance in several significant areas: latency was reduced, throughput was enhanced, resource utilization was more efficient, and operational cost was reduced. These results indicated that our optimization strategies can pay off and bring a number of benefits to NFV and SFC that address a clear need for change in the current type of network management models. Therefore, this paper is valuable and serves as a testament of our strategies' power along with opening various doors for following network optimization research. Therefore, one can see how important it is to implement innovation in the face of change.

关键词： Measurement Technological innovation Costs Machine learning algorithms Heuristic algorithms Machine learning Throughput network function virtualization Resource management Optimization

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AI-based network function virtualization Orchestration

AI-based Network Function Virtualization Orchestration

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IEEE Symposium on network Operations and Management

作者： Hee-Gon Kim Jae-Hyoung Yoo James Won-Ki Hong Computer Science and Engineering Pohang University of Science and Technology Pohang South Korea

ISBN: (数字)9798350327939

ISBN: (纸本)9798350327946

network orchestration is pivotal in automating device, equipment, and service management within the network system. Currently, network function virtualization (NFV) offers immense potential, but the challenge still lies in designing a capable orchestrator for dynamic networks. To address this challenge, we propose an AI-based NFV Orchestration Framework that leverages self-learning capabilities to detect dynamic network changes and make optimal decisions. This framework covers a range of essential functionalities, including NFV Orchestration, VNF Deployment, Service function Chaining (SFC), Auto-Scaling, Migration, Anomaly Detection, Power Management, and Attack & Intrusion Detection. These functions collectively form a comprehensive ML-driven orchestration framework that offers adaptability, intelligence, and efficiency across the entire NFV environment. Our proposed structure aims for zero-touch automation, contributing to the efficient management of dynamic NFV network environments, and making it a compelling solution for the future of networking.

关键词： Autonomous networks Service function chaining Power system management Intrusion detection Machine learning network function virtualization Anomaly detection

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Placement of Deep Reinforcement Learning in Service function Chain of network function virtualization

Placement of Deep Reinforcement Learning in Service Function...

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Data Science and Information System (ICDSIS), IEEE International Conference on

作者： Hongsheng Xu Kaifeng Huang College of Electronic and Commerce Luoyang Normal University Luoyang China Henan key Laboratory for Big Data Processing & Analytics of Electronic Commerce Luoyang Normal University Luoyang China

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

Reinforcement learning is a goal-oriented algorithm. The core goal of the algorithm is to learn to make decisions during the interaction between intelligent agents and the environment. Deep Reinforcement Learning is an algorithm that combines the perception ability of deep learning and the powerful decision-making ability of reinforcement learning. It can directly understand the network and task from experience, thus improving the accuracy and reducing repeated modeling, and is widely used to solve complex problems. A network Service in NFV can be represented as a Service function Chain (SFC), which consists of a series of Virtual network functions (VNF) in a given order. In order to provide efficient services, SFCS are deployed in a number of different servers and direct traffic through a set of VNFS in a specific order. This paper proposes SFC placement in NFV based on deep reinforcement learning model. Finally, it is found that the deep learning model can effectively improve the efficiency of SFC placement.

关键词： Service function chaining Decision making Deep reinforcement learning Stability analysis Reproducibility of results network function virtualization Servers

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Reconfigurable network Slicing Orchestration in network function virtualization Compatible Operational Technology Environment

Reconfigurable Network Slicing Orchestration in Network Func...

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IEEE International Conference on Smart Grid Communications (SmartGridComm)

作者： Brian G. Rodiles Delgado Jared M. Aguayo Abel O. Gomez Rivera Deepak K. Tosh Department of Computer Science University of Texas at El Paso TX USA John Hopkins University Applied Physics Laboratory Laurel MD USA Sandia National Laboratories Albuquerque NM USA

ISBN: (数字)9798350318555

ISBN: (纸本)9798350318562

The ongoing transition to Industry 4.0, which is characterized by increased inter-connectivity of cyber-physical systems, requires having time-sensitive, high throughput, and secure transfer of critical data in industrial sites. In this context, network slicing emerges as a critical tool to ensure timely data delivery by provisioning the network resources to cater to specific applications’ requirements and mitigating potential cyber attacks. To address these challenges, this paper aims to tackle two key questions essential for the successful implementation of network slicing in industrial environments. First, it investigates architectural considerations for developing a network infrastructure capable of supporting network slicing functionalities effectively. The proposed approach significantly improves deployment efficiency over traditional manual configurations. Second, it delves into the automated orchestration process, elucidating the steps and components involved in transitioning from a static network management approach to dynamically leverage network function virtualization schemes for creating network slices in ad-hoc manner. The system demonstrates high throughput suitable for production-level solutions and maintains exceptionally low latency, making it ideal for ultra-reliable low-latency communications. Even with increased network demands, the system remains stable, with effective Quality of Service (QoS) management, ensuring reliable performance under varying conditions. The proposed architecture outlines the necessary components, services, and communication protocols required for a production-level orchestrator for network segmentation in SCADA environments.

关键词： Protocols network slicing Quality of service Manuals Ultra reliable low latency communication Throughput network function virtualization Smart grids Fourth Industrial Revolution Reliability

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FPGAs for network function virtualization: Challenges in Placement and Partitioning

FPGAs for Network Function Virtualization: Challenges in Pla...

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Symposium on Integrated Circuits and Systems Design (SBCCI)

作者： Victor Sberse Guerra Gabriel Luca Nazar Instituto de Informática Universidade Federal do Rio Grande do Sul Porto Alegre Brazil

ISBN: (数字)9798350391695

ISBN: (纸本)9798350391701

In the network function virtualization paradigm, Field Programmable Gate Arrays (FPGAs) can be used to reduce bottlenecks introduced by the substitution of dedicated hardware middleboxes by virtualized implementations. Employing virtual function implementations leads to easier maintenance and deployment, allowing a more flexible infrastructure. However, where to place and how to efficiently share these computational resources are crucial tasks as we seek to achieve the best possible use of the infrastructure. In this work, we will demonstrate the importance of adequate FPGA placement and partitioning in a network to make effective use of the reconfigurable resources.

关键词： Logic gates Middleboxes Hardware network function virtualization Maintenance Computational efficiency Field programmable gate arrays System analysis and design

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DRL-Based Service function Chains Embedding Through network function virtualization in STINs

DRL-Based Service Function Chains Embedding Through Network ...

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Signal, Information and Data Processing (ICSIDP), IEEE International Conference on

作者： Li Li Chuhong Yang Haoyang Li Dongxuan He School of Information and Electronics Beijing Institute of Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Space-terrestrial integrated networks (STINs) are gaining increasing attention for their outstanding benefits in providing seamless connectivity, enhancing network resilience, increasing capacity, and expanding coverage. The combination of network function virtualization (NFV) and deep reinforcement learning (DRL) is a promising candidate to boost the capability of STINs to deliver high-quality services. In this paper, a STIN architecture based on artificial intelligence (AI) algorithms and the process of embedding Service function Chains (SFCs) into highly dynamic STINs are studied. Then, we propose an enhanced DRL-based SFCs embedding algorithm that initiates with a feature fusion phase of links, followed by aggregating the feature vectors of all nodes and their neighboring nodes to form context information. This context information is then used as part of the input state for the DRL-based SFCs embedding algorithm. Simulation results show that the proposed algorithm can effectively reduce the latency of SFCs embedding when compared with other existing algorithms, thus showing the superiority of our proposed algorithm.

关键词： Satellites Service function chaining Heuristic algorithms Simulation Deep reinforcement learning Vectors network function virtualization Reliability Artificial intelligence Resilience

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