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IEEE International Conference on Electronics and Communication Engineering (ICECE)

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

ISBN: (数字)9798331517755

ISBN: (纸本)9798331517762

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.

关键词： Firewalls (computing) Programming Routing Hardware Data models Registers System-on-chip network function virtualization Logic Optimization

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Breaking the Limits: Bio-Inspired SFC Deployment across Multiple Domains, Clouds and Orchestrators

Breaking the Limits: Bio-Inspired SFC Deployment across Mult...

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

作者： Vinicius Fulber-Garcia José Flauzino Giovanni Venâncio Alexandre Huff Elias P. Duarte Junior UFPR Federal University of Paraná Curitiba Brazil UTFPR Federal Technological University of Paraná Toledo Brazil

ISBN: (数字)9798350380538

ISBN: (纸本)9798350380545

The Multi-SFC strategy enables the deployment of virtual network services across multiple clouds, domains, and NFV orchestrators. In this context, finding optimal solutions to the NFV Resource Allocation (NFV-RA) problem, and in particular the VNF Forwarding Graph Embedding (VNF-FGE) problem, becomes a formidable challenge. This translates into a multi-objective optimization problem that involves multiple heterogeneous resources, services and functions, each with particular sets of features and restrictions. In this work, we propose a bioinspired strategy for mapping Multi-SFCs, which consists of a multi-objective genetic algorithm that minimizes inter-domain latency and resource usage costs, while maximizing inter-domain bandwidth availability. Furthermore, the solution is dynamic in the sense that service re-mapping can be invoked after network conditions change. Simulation results show the effectiveness of the proposal to create and adapt optimized solutions.

关键词： Cloud computing Costs Simulation network function virtualization Resource management Proposals Software defined networking Optimization Monitoring Genetic algorithms

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ePEM: An End-to-End Proactive Secure Connectivity Manager for 6G Orchestrator Solutions

ePEM: An End-to-End Proactive Secure Connectivity Manager fo...

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IEEE International Workshop on Computer-Aided Modeling, Analysis, and Design of Communication Links and networks, CAMAD

作者： Alessandro Carrega Ramin Rabbani Department of Electrical Electronic and Telecommunications Eng. Naval Architecture (DITEN) University of Genoa (UniGe) Italy Department of Electrical Electronic and Telecommunications Eng. Telecommunication Networks and Telematics Lab (TNT-Lab) Naval Architecture (DITEN) University of Genoa (UniGe) Italy

ISBN: (数字)9798350377644

ISBN: (纸本)9798350377651

The End-to-End Proactive Security Connectivity Manager (ePEM) system is introduced as a cutting-edge connectivity manager designed to orchestrate and secure end-to-end services within the Holistic, Omnipresent, Resilient Services for future 6G wireless and computing Ecosystems (HORSE) security infrastructure for future Sixth-Generation (6G) networks. It plays a pivotal role in managing network function virtualization (NFV) and applicative services, providing observability and data simplification to improve decision-making and security management. ePEM employs meta-actions for security contingency planning and maintains a comprehensive database of the network's logical topology. Built on a modular architecture, it supports various network function (xNF) and ecosystems, using blueprint profiles to standardise the operations for network elements.

关键词： 6G mobile communication Wireless communication Automation Databases Security management Ecosystems Decision making Computer architecture network function virtualization Security

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A CPN-based Model for Resource Allocation in Multi-Access Edge Computing Supporting URLLC

A CPN-based Model for Resource Allocation in Multi-Access Ed...

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

作者： Caio Souza Renata Dos Reis Maria Lima Damasceno Marcos Falcão Andson Balieiro Centro de Informática (CIn) Universidade Federal de Pernambuco (UFPE) Recife Brazil

ISBN: (数字)9798350380538

ISBN: (纸本)9798350380545

Although the integration of Multi-Access Edge Computing (MEC) and network function virtualization (NFV) paves the way for supporting URLCC services in 5G and beyond 5G networks, it raises some practical issues such as limited resources of edge nodes, overhead caused by virtualization, and failures during service processing, which may lead to URLLC requirement violations. By incorporating these features, this work proposes a Coloured Petri Net (CPN)-based model to address the dynamic resource allocation for URLLC services in NFV-MEC-based 5G networks. Additionally, to mitigate the virtualization overhead, a resource pre-initialization strategy is employed in consonance with service buffering and resource scaling on demand. Results showed that resource pre-initialization has positive impacts on overall system performance without significantly affecting power consumption, achieving similar gains to those achieved by setup rate improvements.

关键词： Multi-access edge computing Power demand 5G mobile communication System performance Petri nets Ultra reliable low latency communication network function virtualization Resource management virtualization Software defined networking

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MATADOR: ML-based Cloud Gaming Traffic Detection entirely in Programmable Hardware

MATADOR: ML-based Cloud Gaming Traffic Detection entirely in...

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

作者： Suneet Kumar Singh Christian Esteve Rothenberg Alireza Shirmarz Fábio Luciano Verdi Israat Haque Gyanesh Patra Gergely Pongrácz Universidade Estadual de Campinas (UNICAMP) Brazil Norwegian University of Science and Technology Norway Federal University of São Carlos Brazil Dalhousie University Canada Ericsson Research Hungary

ISBN: (数字)9798350380538

ISBN: (纸本)9798350380545

Cloud gaming (CG) is gaining increasing interest as a new approach to delivering high-quality gaming anywhere, anytime, and on diverse devices. To improve the gaming experience, CG traffic needs to be monitored and prioritized to avoid high latency and jitter. Recent attempts to classify CG traffic on cloud nodes are time- and resource-consuming. Improving CG detection time and accuracy is critical to ensuring a responsive and seamless experience. This paper proposes MATADOR, a machine learning system that classifies CG traffic entirely in P4 programmable hardware. Our design and implementation using a Tofino switch overcome P4 challenges for hardware targets. The performance is analyzed for Tofino and a Python-based simulator. The obtained results demonstrate that, with an accuracy of around 97%, our approach significantly reduces the CG detection time when compared against alternative approaches.

关键词： Accuracy Switches Machine learning Cloud gaming Jitter Hardware network function virtualization Software defined networking Monitoring

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QoE Evaluation for Emerging Media Applications: network-Level Analysis and Traffic Modeling

QoE Evaluation for Emerging Media Applications: Network-Leve...

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

作者： Md Tariqul Islam Christian Esteve Rothenberg Universidade Estadual de Campinas Brazil

ISBN: (数字)9798350380538

ISBN: (纸本)9798350380545

Technological advancements are driving the growing popularity of Emerging Media Applications (EMA), ranging from mobile applications to immersive services in eXtended Reality (XR). However, delivering satisfactory Quality of Experience (QoE) for EMA over 5G and beyond networks remains a relevant yet challenging topic due to high computational and network resource requirements and the need for network-level optimization. This PhD research addresses QoE evaluation for EMA in several directions through network-level measurement, traffic modeling, and optimization. It outlines the goals, methodologies, and potential research plans for each area.

关键词： Analytical models Extended reality Computational modeling Media Distance measurement network function virtualization Mobile applications Quality of experience Software defined networking Optimization

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A Development of Dynamic End-To-End Slicing network Along with the Offline SLA-Method Integrated with Deep Learning for 5G/6G networks

A Development of Dynamic End-To-End Slicing Network Along wi...

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Innovative Sustainable Technologies for Energy, Mechatronics, and Smart Systems (ISTEMS), International Conference on

作者： Abdul Khalid Raghu G Anand Ashwini Malviya Department of IT M. Tech Integrated Noida Institute of Engineering & Technology Greater Noida Uttar Pradesh India Department of Management School of Mangement - UG JAIN (Deemed to be University) Bangalore Karnataka India Department of uGDX ATLAS SkillTech University Maharashtra India

ISBN: (数字)9798350384246

ISBN: (纸本)9798350384253

Using SDN, or software-defined networking, and network function virtualization, or (NFV), we address the problem of resource provisioning in fifth-generation (5G) networks to enable end-to-end dynamic slicing. The core of our method is to dynamically assign separate portions of Physical Resource Bricks (PRBs), basic processing resources, transport capacity, and components like The information Forwarding Elements (DFE) plus network connections to tenants, logical operators of different slices. We use large datasets of key performance indicators (also known as KPIs) taken from a live mobile phone network fitted with traffic sensors in order to create a forecast model for the traffic of slices. We offer a technique that makes use of a soft-gated Randomised Unit (GRU) based low-complexity slices traffic prediction. Furthermore, we build joint multi-slice DNN s (deep neural networks) for each virtual network role and train them to predict the needed resources, guaranteeing adherence to two crucial service level contracts (SLAs): resource bounds-based SLA and violation rate-based SLA. We present dataset-dependent generalised non-convex constraints as a solution to the difficulty of optimisation challenges related to DNNs. The offline optimisation procedure of the DNNs, which is solved using a non-zero summing two-player game strategy, smoothly incorporates these limitations. Throughout our methodology, we stress the importance of underlying hyperparameters that in striking a balance between the trade-off between overstocking and preserving slices' isolation. Lastly, we do a thorough closed-form analysis including reliability theory in order to determine the bottom limit of the reliable convergent probability. In addition, we investigate how the violation rate affects this convergence probability, offering important information on the dependability of our suggested system.

关键词： Deep learning Artificial neural networks Reliability theory Predictive models Dynamic scheduling Aerodynamics network function virtualization

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Latency-Aware Resource Utilization Optimization for Service function Chain Deployment in Heterogeneous Edge network

SSRN

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SSRN 2024年

作者： Liao, Zhuofan Xiong, Qingli Huang, Mingfeng Tang, XiaoYong School of Computer and Communication Engineering Changsha University of Science and Technology Hunan Changsha410114 China

network function virtualization (NFV) improves service quality by converting traditional hardware-supported network services into Virtualized network functions (VNF), thereby improving network's scalability. One application form of this technology is the deployment of Service function Chaining (SFC) in Edge Computing (EC) scenario to establish NFV-EC network. However, in the context of NFV-EC, the deployment of SFC suffers low resource utilization of network nodes, which is attributed to the heterogeneity in the distribution of resource in edge network. In this case, we investigate a Service function Chain Deployment (SFCD) problem to maximize resource utilization rate, with the constraint of End-to-End (E2E) latency. As this problem is NP-hard, many existing studies have employed meta-heuristic algorithms to tackle it. However, these algorithms are not suitable for our problem, due to their drawbacks in the optimization objective or the problem scenario. Base on the characteristics of Genetic Algorithm (GA) and Ant Colony Optimization (ACO), a two-stage algorithm, named Resource-Optimized and Latency-Aware (ROLA) algorithm, is proposed to simultaneously improve resource utilization rate and latency. This approach involves a customized GA for narrowing the search space and an improved ACO for optimization, which migitate their drawbacks while utilizing the advantages of them. The experiment results indicate that, in comparison to D-GA, FH-ACO, and B-GWO, \emph{ROLA} achieved superior performance in resource utilization rate, propagation latency, acceptance rate, and execution time. © 2024, The Authors. All rights reserved.

关键词： network function virtualization

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Federated Transfer Component Analysis Towards Effective VNF Profiling

Federated Transfer Component Analysis Towards Effective VNF ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Xunzheng Zhang Shadi Moazzeni Juan Marcelo Parra-Ullauri Reza Nejabati Dimitra Simeonidou High Performance Networks Research Group Smart Internet Lab Faculty of Engineering University of Bristol Bristol UK

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

The increasing concerns of knowledge transfer and data privacy challenge the traditional gather-and-analyse paradigm in networks. Specifically, the intelligent orchestration of Virtual network functions (VNFs) requires understanding and profiling the resource consumption. However, profiling all kinds of VNFs is time-consuming. It is important to consider transferring the well-profiled VNF knowledge to other lack-profiled VNF types while keeping data private. To this end, this paper proposes a Federated Transfer Component Analysis (FTCA) method between the source and target VNFs. FTCA first trains Generative Adversarial networks (GANs) based on the source VNF profiling data, and the trained GANs model is sent to the target VNF domain. Then, FTCA realizes federated domain adaptation by using the generated source VNF data and less target VNF profiling data, while keeping the raw data locally. The proposed FTCA enables efficient profiling knowledge transfer among different VNFs, while maintaining data privacy. Through FTCA, faster new VNF deployment can be expected. Experiments show that the proposed FTCA can effectively predict the required resources for the target VNF. Specifically, the RMSE index of the regression model decreases by 38.5% and the R-squared metric advances up to 68.6%.

关键词： Measurement Data privacy Adaptation models Transfer learning Generative adversarial networks Data models network function virtualization Global communication Knowledge transfer

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StateProc: Empowering network functions with Enhanced Processing Capabilities in Edge Clouds

StateProc: Empowering Network Functions with Enhanced Proces...

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International Symposium on networks, Computers and Communications (ISNCC)

作者： Mahdi Attawna Tung V. Doan Frank H.P. Fitzek Giang T. Nguyen Deutsche Telekom Chair of Communication Networks TU Dresden Germany Haptic Communication Systems TU Dresden Germany Centre for Tactile Internet with Human-in-the-Loop (CeTI)

ISBN: (数字)9798350364910

ISBN: (纸本)9798350364927

Edge clouds embrace network function virtualization (NFV) to bring network functions (NFs) closer to end devices. The mobility nature of edge clouds and the emergence of new use cases, such as robot control or metaverse, demand NFs with high resilience. However, numerous NFs like firewalls require stateful processing that depends on historical processing values, referred to as NF states. Consequently, supporting stateful NFs with high resilience necessitates the maintenance of their states, often resulting in significant modification of the NFs. While providing high resilience for NFs, maintaining their processing performance is a critical challenge for existing solutions. Our proposed framework, StateProc, eliminates this concern by enhancing the processing capabilities of NFs through the utilization of NF states for custom processing. Through custom processing for state transfer, the evaluation results show that StateProc significantly improves the state transfer time, up to 93% faster than a notable state-of-the-art framework, without additional processing overhead.

关键词： Computers Cloud computing Metaverse Firewalls (computing) Robot control network function virtualization Maintenance Noise measurement Logic Resilience

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