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Proceedings of the 3rd International Conference on Electronic Information Technology and Smart Agriculture

作者： Wang Zeng Chang Liu Ruojun Liao Shunru Zhang School of Computer and Artificial Intelligence Huaihua University China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua University China School of Intelligent Engineering Guangdong AIB Polytechnic China Huaihua Key Laboratory of Agricultural Ecology Prevention and Control Huaihua University China

ISBN: (纸本)9798400716775

Considering the heterogeneity of computing and networking resources in the edge environment, this paper designs a service chain deployment system based on software-defined network and network function virtualization. It adopts a low-coupling layered design, which is divided into management, operation, and infra-structure layers from top to bottom. The management layer arranges node map-ping and routing according to certain policies, while the operation layer defines the entire service chain operation interface. The controller and related proxy services are designed and utilized to achieve global monitoring of the physical net-work and nodes, supporting fine-grained adjustment of virtual network functions and routing. Communication between virtual nodes can select different channels over the underlying network according to their needs, achieving logical isolation while supporting directed graph deployment. An online deployment method adapted to this system is designed to balance the load of each node and achieve greater capacity while satisfying latency constraints.

关键词： Edge computing network function virtualization Service function chain Software defined network

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On End-to-End Protection of Service function Chain

On End-to-End Protection of Service Function Chain

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International Conference on Information and Communication Technologies for Disaster Management (ICT-DM)

作者： Mohand Yazid Saidi L2TI Institut Galilée Université Sorbonne Paris Nord Villetaneuse France

ISBN: (数字)9798350367928

ISBN: (纸本)9798350367935

network function virtualization (NFV) is transforming network architectures by decoupling network functions from proprietary hardware, enabling their implementation as software instances on cost-effective commodity servers. This paradigm shift enables on-demand service creation through the dynamic chaining of Virtual network functions (VNFs). However, ensuring service continuity in NFV environments presents unique challenges. This paper addresses the critical issue of end-to-end 1+1 protection for Service function Chains (SFCs) by proposing a novel approach based on calculating two disjoint provisioning paths. We introduce an innovative heuristic algorithm that iteratively computes k successive provisionings, where k is a fixed parameter, first maximizing protection level and then minimizing resource utilization. Our approach is founded on the principle that each newly computed provisioning improves the existing solution in terms of either protection ratio or cost efficiency. Through extensive simulations on diverse network topologies, we validate our heuristic’s convergence and effectiveness. Results demonstrate significant improvements in both protection coverage and cost optimization compared to traditional methods that simply determine two maximally disjoint paths.

关键词： Costs Service function chaining Simulation Software network function virtualization Reliability Servers Resource management Protection Optimization

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SFC Orchestration and SDN-VNF Trusted Deployment Scheme Based on Deep Reinforcement Learning Algorithm

SFC Orchestration and SDN-VNF Trusted Deployment Scheme Base...

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International Conference on Computational Intelligence and Communication networks (CICN)

作者： Yongliang Li College of Electronic and Commerce Luoyang Normal University Luoyang China

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505271

This paper introduces the background knowledge of network function virtualization, SDN, NFV, service function chain and related resource allocation problems in VNF, and explains the principles of reinforcement learning algorithms including Q-learning, A3C algorithm and DDQN algorithm in detail. Software Defined network (SDN) separates the network data forwarding function, and the control plane is responsible for formulating and issuing the structured data packet forwarding rules according to the needs of network users. Compared with other algorithms, A3C algorithm can significantly reduce the requirement of hardware resources. This paper presents SFC orchestration and SDN-VNF trusted deployment scheme based on deep reinforcement learning algorithm. Firstly, the layout scheme of SDN-VNF based on A2C and DQN algorithm is compared. Finally, the efficiency of A3C and DDQN algorithms in SFC orchestration scheme is compared. Experimental results show that the proposed deep reinforcement learning algorithm is effective in SFC Orchestration and SDN-VNF.

关键词： Q-learning Service function chaining Heuristic algorithms Software algorithms Computer architecture Deep reinforcement learning Real-time systems network function virtualization Resource management Edge computing

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NFVDC: A flexible and efficient NFV platform for dynamic service chaining

NFVDC: A flexible and efficient NFV platform for dynamic ser...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Qiuwen Lu Qingyun Liu Binxing Fang Institute of Information Engineering Chinese Academy of Sciences Beijing China National Engineering Research Center of Information Security Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Harbin Institute of Technology Shenzhen China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

This paper presents the design and implementation of a novel network function virtualization (NFV) platform, NFVDC, specifically for dynamic service chaining. Unlike traditional service chaining methods, NFVDC supports real-time, reconfigurable service chains that accommodate service functions dynamically joining or leaving the chain. By integrating an advanced traffic steering method, optimized CPU scheduling, and a causal message-passing mechanism, NFVDC addresses challenges in deploying dynamic service chaining, such as traffic steering, resource scheduling, and session state management. Our evaluation demonstrates that NFVDC’s forwarding performance nearly doubles compared to existing solutions when extending service chain lengths to six.

关键词： Distributed processing Scalability Refining Dynamic scheduling Robustness Real-time systems network function virtualization

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Enhancing Virtualized network functions through Advanced Machine Learning for Efficient Resource Allocation and Dynamic Placement

Enhancing Virtualized Network Functions through Advanced Mac...

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ACS/IEEE International Conference on Computer Systems and Applications

作者： Khaled Ahmed Gadouh Hend Koubaa Sellami The National School of Electronics and Telecommunication Sfax Tunisia

ISBN: (数字)9798331518240

ISBN: (纸本)9798331518257

This paper addresses the challenges of network functions virtualization (NFV) with a specific focus on the optimization of Virtualized network functions (VNFs) through advanced machine learning techniques. As NFV evolves to support modern network demands, including the upcoming transition from 5G to 6G technologies, efficient resource allocation and dynamic VNF placement become crucial. Traditional methods often fail to meet these requirements due to their static nature and inability to adapt to changing network conditions dynamically. we introduce a comprehensive framework including the integration of multiple machine learning models (i.e., Random Forest, Decision Trees, SVR) combined into ensemble approach such as Linear regression to predict and optimize VNF performance dynamically. This scheme not only improves the reliability of resource placement, but also is conducive to realizing the automatic scalable VNF deployment for ZSM: Zero-touch network and Service Management. Here, all of these highlight that the ensemble model is better than the individual predictive machine models in predicting/amodeling network conditions as well as adjusting resources efficiently. The proposed model achieves very good results, improving in all the relevant key performance metrics RMSE, R2 and Explained Variance), and thus demonstrating that machine learning can bring deep changes to NFV environments. Though the study demonstrates the potential amplitude of these models, it also reveals significant challenges in bringing them to deployment levels, in specific extreme MAPE (Mean Absolute Percentage Error) cases. As such it opens doors for more research and development into this area too This work aims to expand the horizon of intelligent network management through actionable knowledge and guides from machine learning on improving the effectiveness and efficiency of network services supporting further adaptive, user-oriented networking schemes.

关键词： Measurement Adaptation models Autonomous networks Machine learning Predictive models network function virtualization Resource management Reliability Ensemble learning Research and development

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Programmable network Testbed for QoS/QoE Assessment of Holographic Media Delivery

Programmable Network Testbed for QoS/QoE Assessment of Holog...

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IEEE Conference on network function virtualization and Software Defined networks (NFV-SDN)

作者： Alan Teixeira Da Silva Rafael P. Silva Clerici Fabricio E. Rodriguez Cesen Md Tariqul Islam Christian Esteve Rothenberg Universidade Estadual de Campinas (UNICAMP) Brazil

ISBN: (数字)9798350380538

ISBN: (纸本)9798350380545

Transmitting holographic media is one of the driving applications in the field of immersive media, demanding moving beyond 5G capabilities. The effective delivery of HolographicType Communications (HTC) presents challenging network and computational requirements to meet the end user’s expected Quality of Experience (QoE). This demo showcases ongoing efforts on a programmable testbed to facilitate research on the impact of network Quality of Service (QoS) parameters on Point Cloud video QoE metrics at the Head Mounted Device (HMD).

关键词： Point cloud compression Measurement 5G mobile communication Quality of service Resists Media network function virtualization Quality of experience Software defined networking

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Multi-Objective Optimization of Open RAN Data Center Placement for Enhanced 5G Deployments

Multi-Objective Optimization of Open RAN Data Center Placeme...

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IEEE Conference on network function virtualization and Software Defined networks (NFV-SDN)

作者： Daniel R. de Luna Fuad M. Abinader Paulo Ricardo Branco Da Silva Michel Bernado de Paiva Vicente A. de Sousa Augusto J. Venâncio Neto Federal University of Rio Grande do Norte (UFRN) Natal Brasil Centro de Pesquisa e Desenvolvimento em Telecomunicações (CPQD) Campinas Brazil

ISBN: (数字)9798350380538

ISBN: (纸本)9798350380545

This paper tackles the optimization of placement and quantity of Open RAN Data Centers (ODCs) for hosting virtualized Open Distributed Units (O-DUs) in 5G NR networks, promoting flexible and scalable SDN architectures. Utilizing a Multi-Objective Facility Location Problem (MO-FLP) framework, we aim to minimize infrastructure costs related to network function virtualization (NFV) while meeting strict fronthaul latency and processing capacity requirements. Our method employs Non-dominated Sorting Genetic Algorithms (NSGA-II) to manage these trade-offs, achieving significant cost-efficiency and network performance gains through extensive city-wide 5G NR deployment simulations. We validated our approach in Natal and Manaus, evaluating ODC locations, capacities, and fiber usage. This study offers new insights and practical strategies for efficient 5G NR deployments, addressing key challenges in modern telecommunications infrastructure.

关键词： Data centers Costs 5G mobile communication Urban areas Open RAN Optical fiber networks network function virtualization Telecommunications Optimization Genetic algorithms

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Optimizing Cache Management Policy for Fast Packet Processing in NFV Data Plane 7

Optimizing Cache Management Policy for Fast Packet Processin...

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7th IEEE International Conference on Electronics and Communication Engineering, ICECE 2024

作者： Wang, Runfa Wu, Qiang Nanjing University of Aeronautics and Astronautics Dept. of Computer Science and Technology Nanjing China

ISBN: (纸本)9798331517755

Establishing efficient caching in the data plane is crucial for the performance of packet forwarding for network function virtualization(NFV). For switches that provide network functions such as forwarding, routing, and firewall, the on-chip memory in the data plane is closest to the packet processing logic. However, due to hardware constraints or limitations in the programming model, the programmability and memory access capabilities of data plane caching in many network devices, such as programmable switches, smart network cards, and DPUs, are often restricted. As a result, most existing data plane caches rely on simple and inefficient replacement strategies. In this paper, an analysis of the limitations in programmability of the data plane is conducted, and an algorithm with bit-slice operators is proposed to determine the update and eviction strategy for each item in the cache array. Leveraging the principles of limited associativity design, the storage structure of the ternary match kind identifier (TCAM) table is optimized due to constraints on the number of mask bits. Efficient utilization of cache hits is achieved by setting timestamps and packet flow identifiers in registers. Experimental results demonstrate that our caching strategy exhibits significant improvements in hit rate and latency performance compared to some previously proposed approaches. © 2024 IEEE.

关键词： network function virtualization

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A new Framework for Failure Classification in 5G Core network

A new Framework for Failure Classification in 5G Core Networ...

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International Conference on Communications and networking, ComNet

作者： Damos Ayobo Abongo Asma Kehila Safa Cherif Rahma Bouraoui Zaineb Labidi EspritTech Esprit School of Engineering Ariana Tunisia

ISBN: (数字)9798331520793

ISBN: (纸本)9798331520809

Considering the demands for high bandwidth, low latency, and high reliability, 5G network systems need continuous and high-quality operation to mitigate the consequences of service failures. To meet the diverse service requirements of 5G use cases, a cloud-based architecture based on NFV (network function virtualization) and SDN (Software Defined networks) technologies has been implemented. This has increased the complexity, workload and uncertainty of the operation and maintenance environment. Historically, fault management and other associated maintenance activities have been handled manually by humans. However, in the 5G era, relying entirely on humans will not be enough to cope with more demanding and complex network operation activities. A more automated approach to quickly analyze millions of network data to detect, classify or predict failures is thus needed. In this paper, we propose a ML-based system for autonomous 5G Core network failure detection and classification. In addition, a comparative analysis of different applied machine learning/deep learning algorithms is presented.

关键词： Uncertainty Machine learning algorithms 5G mobile communication Machine learning Prediction algorithms network function virtualization Maintenance Software reliability Software defined networking network systems

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Improving Orchestration Service Using gRPC API and P4-Enabled SDN Switch in Cloud Computing Platform: An OpenStack Case

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2023年第3期50卷 1083页

作者： Hari Krishna, S.M. Sharma, Rinki Department of Computer Science and Engineering M S Ramaiah University of Applied Sciences Bengaluru India Department of Computer Science and Engineering M S Ramaiah University of Applied Sciences Bengaluru India

Cloud computing platforms like OpenStack use orchestration as a crucial component in the deployment and administration of cloud services. For the deployment and management of virtualized resources, these platforms mainly rely on orchestration techniques. Cloud orchestration services like Service function Chaining (SFC) have benefited from lower costs and increased scalability for cloud computing platforms thanks to the introduction of technologies like Software-Defined networking (SDN) and network function virtualization (NFV). The use of gRPC APIs and an SDN switch with P4 support is described in this research as an innovative method for enhancing SFC orchestration service in OpenStack computing platforms. The proposed approach uses a P4 enabled SDN switch and gRPC APIs to enhance network performance in data plane communication with the underlying infrastructure and performance in terms of service creation time between the SFC orchestration services. Our evaluation's findings demonstrate that this strategy greatly boosts the orchestration service's performance, making it more appropriate for usage in extensive cloud-based deployments. Additionally, the possibility of combining gRPC APIs and P4 switch to improve the efficiency and flexibility of orchestration service in cloud computing platforms is demonstrated by the performance analysis of the proposed approach on numerous platforms across diverse use cases. © (2023), (International Association of Engineers). All Rights Reserved.

关键词： network function virtualization

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