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Joint 30th European Conference on networks and Communications / 3rd 6G Summit (EuCNC/6G Summit)

作者： Xie, Min Michelinakis, Foivos Dreibholz, Thomas Pujol-Roig, Joan S. Malacarne, Sara Majumdar, Sayantini Poe, Wint Yi Elmokashfi, Ahmed M. Telenor Res Telenor Norway Simula Metropolitan Ctr Digital Engn Oslo Norway Samsung Elect R&D Inst Staines Surrey England Huawei Technol Munich Res Ctr Munich Germany

ISBN: (纸本)9781665415262

Artificial Intelligence (AI) is widely applied in telecommunications to enable zero-touch automation in network operation and service management. Due to the high complexity, deploying advanced AI mechanisms is not always feasible inside the operator's network domains. Instead, via service exposures, it becomes possible for vertical customers to integrate their external AI solutions with the network and service management system to form a closed loop (CL) and contribute to the automation process. In this paper, we propose an exposed CL model based on service exposure and apply it to automate service assurance tasks like auto-scaling in a network function virtualization (NFV) system orchestrated by ETSI Open Source MANO (OSM). A testbed is built to validate the model. It collects monitoring data from the OSM monitoring module and external monitoring tools. Vertical customers drive and customize their AI solutions to aggregate these data sets and run analytics to detect and predict anomalies prepared for scaling. Preliminary analysis demonstrates the added values of customer-driven monitoring and analysis via the exposed CL.

关键词： Analytical models Automation Tools Data models network function virtualization Complexity theory Telecommunications

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Deterministic Service function Chaining over Beyond 5G Edge Fabric

Deterministic Service Function Chaining over Beyond 5G Edge ...

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IEEE Global Communications Conference (GLOBECOM)

作者： Yu, Hao Taleb, Tarik Zhang, Jiawei Aalto Univ Espoo Finland Oulu Univ Oulu Finland Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (纸本)9781728181042

Along with the increasing demand for latency-sensitive services and applications, Deterministic network (DetNet) concept has been recently proposed to investigate deterministic latency assurance for services featured with bounded latency requirements in 5G edge networks. The network function virtualization (NFV) technology enables Internet Service Providers (ISPs) to flexibly place Virtual network functions (VNFs) achieving performance and cost benefits. Then, Service function Chains (SFC) are formed by steering traffic through a series of VNF instances in a predefined order. Moreover, the required network resources and placement of VNF instances along SFC should be optimized to meet the deterministic latency requirements. Therefore, it is significant for ISPs to determine an optimal SFC deployment strategy to ensure network performance while improving the network revenue. In this paper, we jointly investigate the resource allocation and SFC placement in 5G edge networks for deterministic latency assurance. We formulate this problem as a mathematic programming model with the objective of maximizing the overall network profit for ISP. Furthermore, a novel Deterministic SFC deployment (Det-SFCD) algorithm is proposed to efficiently embed SFC requests with deterministic latency assurance. The performance evaluation results show that the proposed algorithm can provide better performance in terms of SFC request acceptance rate, network cost reduction, and network resource efficiency compared with benchmark strategy.

关键词： Deterministic networks Service function Chain network function virtualization Quality of Service Resource Orchestration

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KSN: Modeling and simulation of knowledge using machine learning in NFV/SDN-based networks

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SIMULATION MODELLING PRACTICE AND THEORY 2022年 121卷

作者： Nikbazm, Rojia Ahmadi, Mahmood Razi Univ Comp Engn & Informat Technol Dept Kermanshah Iran

The rapid advancement of networking and computing technologies has led to the emergence of a wide range of diverse dynamic network services that present networks with new challenges, including in the provision and management of services. Recently, SDN and NFV technologies can be effective solutions in the dynamic management of network functions. For dealing with dynamic and complex problems in the field of NFV and SDN issues, the use of knowledge-based systems can be a good solution that has received more and more attention from researchers and developers. Despite the increasing attention to the use of knowledge such as machine learning and deep learning methods in the NFV and SDN field, the lack of a simulation framework with the ability to knowledge concept to implement and evaluate new methods has created a gap. In order to evaluate a new method on real platforms, a large-scale infrastructure has to be used, which faces challenges such as availability, cost, time and implementation difficulties. Since the existing simulation frameworks for networks using NFV and SDN technologies (e.g. CloudsimSDN-NFV) do not support the knowledge-based concept, to fill this gap, we propose a highly extensible, interoperable, and scalable simulation framework for developing knowledge-based methods such as machine learning algorithms in the field of NFV and SDN, such as virtualized network functions auto-scaling, placement and migration. Also, for the proposed Knowledge-defined SDN/NFV (KSN) framework, an architecture is provided by defining the knowledge plane in the SDN/NFV integration structure. In addition, a new auto-scaling method based on the prediction of NFV resources consumption using machine learning has been proposed and simulated to demonstrate the capabilities of the framework and its evaluation. As we know, this is the first machine learning-based method in the NFV field, including VNF auto-scaling, which has been simulated in a network environment, and the impact of

关键词： Knowledge-defined SDN/NFV network network function virtualization Machine learning VNF auto-scaling VNF resource prediction

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Asynchronous Coarse-Grained Load Migration Scheme for IoT Applications in Fog networks

Asynchronous Coarse-Grained Load Migration Scheme for IoT Ap...

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IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT)

作者： Jasim, Mohammed A. Siasi, Nazli Almalag, Mohammad Honary, Vahraz Aldalbahi, Adel Univ Mt Union Sch Engn Alliance OH 44601 USA Christopher Newport Univ Dept Phys Comp Sci & Engn Newport News VA 23606 USA King Faisal Univ Dept Elect Engn Al Hufuf Saudi Arabia

ISBN: (纸本)9781665438414

Fog computing provides distributed processing and storage solutions for real-time applications that demand low latency and fast response times. This makes fog solutions suitable for internet-of-things (IoT) devices that request various network functions. To offer multiple functions for IoT applications, fog nodes can leverage network function virtualization (NFV) for a scalable and elastic function modification, i.e., without the need for dedicated hardware. Despite the saliencies achieved from the synergistic combination between fog and NFV technologies, a key challenge here is the limited resources at the fog nodes. This makes the latter susceptible to rapid node saturation and network congestion at high traffic volumes. Along this, efficient resource utilization and load distribution mechanisms are necessary to enhance admission rates and quality-of-service (QoS). Hence, this paper proposes novel load migration schemes for NFV-based fog networks that aim to reduce the overhead of the migration process. Namely, a coarse-grained load diffusion scheme is adopted to reduce migration frequencies, incurred delay, and cost. Further, destination nodes are selected based on least-load (LL) or least-delay (LD) mechanisms to accommodate delay-sensitive, delay-tolerant, and computation-intensive IoT applications.

关键词： Coarse-grained fog computing least-delay least-load load migration migration frequency network function virtualization

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DRL-Deploy:Adaptive Service function Chains Deployment with Deep Reinforcement Learning 19

DRL-Deploy:Adaptive Service Function Chains Deployment with ...

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19th IEEE International Symposium on Parallel and Distributed Processing with Applications (IEEE ISPA)

作者： Wei, Xiaohui Sheng, Yong Li, Lina Zhou, Changbao Jilin Univ Coll Comp Sci & Technol Changchun Jilin Peoples R China Changchun Univ Coll Comp Sci & Technol Changchun Jilin Peoples R China

ISBN: (纸本)9781665435741

With the development of software-defined networking (SDN) and network function virtualization (NFV), the service function chain (SFC) has become a popular paradigm for carrying and completing network services. In this new computing and networking paradigm, virtual network functions (VNFs) are deployed in software entities/virtual machines through physical device networks in a flexible manner to improve resource utilization and reduce management effectiveness. In this case, it is critical to effectively deploy SFCs within an acceptable time to improve quality of service, while meeting the constraints of the physical network. In this paper, we propose an adaptive deep reinforcement learning based method for the online deployment of SFC requests with different QoS requirements, called DRL-Deploy. DRL-Deploy integrates graph convolutional neural networks to effectively extract physical network features and then adopts a parallel method to improve training efficiency, which can converge to the best state. We compare with existing benchmarks, and extensive experiment results show that DRL-Deploy outperforms all others in terms of acceptance rate and long-term average revenue by 8.6% and 22.9%, respectively, while reducing long-term average cost by 36.4%.

关键词： Deep reinforcement learning network function virtualization software-defined networks SFC deployment graph convolutional network

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Demo: Application Monitoring as a network Service 41

Demo: Application Monitoring as a Network Service

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41st IEEE International Conference on Distributed Computing Systems (ICDCS)

作者： Elsaadawy, Mona Fesselier, Laetitia Kemme, Bettina McGill Univ Sch Comp Sci Montreal PQ Canada

ISBN: (纸本)9781665445139

The recent rise of cloud applications, representing large complex modern distributed services, has made performance monitoring a major issue and a critical process for both cloud providers and cloud customers. Many different monitoring techniques are used such as tracking resource consumption, performing application-specific measures or analyzing message exchanges. Typically the collected data is logged at the host on which the application is deployed, then either analyzed locally or forwarded to a remote analysis host. In contrast, this demonstration paper presents a Monitoring as a Service prototype that uses the advances in Software Defined networking (SDN) to move some of the logging functionality into the network. The core of our MaaS is implemented as a virtual network function where agents are co-located with software switches in order to extract performance metrics from the message flows between components in a non-intrusive manner and send the calculated measures to the clients for visualization in near real-time. The MaaS has a lot of flexibility in how it is deployed and does not require to instrument software or platforms. In our demo we show the tool in action demonstrating how users can choose to monitor different service types and performance metrics in a user-friendly manner.

关键词： Application performance monitoring Software defined networking network function virtualization

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Don't Yank My Chain: Auditable NF Service Chaining 18

Don't Yank My Chain: Auditable NF Service Chaining

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18th USENIX Symposium on networked Systems Design and Implementation

作者： Liu, Guyue Sadok, Hugo Kohlbrenner, Anne Parno, Bryan Sekar, Vyas Sherry, Justine Carnegie Mellon Univ Pittsburgh PA 15213 USA Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9781939133212

Auditing is a crucial component of network security practices in organizations with sensitive information, such as banks and hospitals. Unfortunately, networkfunction virtualization (NFV) is viewed as incompatible with auditing practices which verify that security functions operate correctly. In this paper, we bring the benefits of NFV to security-sensitive environments with the design and implementation of AuditBox. AuditBox not only makes NFV compatible with auditing, but also provides stronger guarantees than traditional auditing procedures. In traditional auditing, administrators test the system for correctness on a schedule, e.g., once per month. In contrast, AuditBox continuously self-monitorsfor correct behavior, proving runtime guarantees that the system remains in compliance with policy goals. Furthermore, AuditBox remains compatible with traditional auditing practices by providing sampled logs which still allow auditors to inspect system behavior manually. AuditBox achieves its goals by combining trusted execution environments with a lightweight verified routing protocol (VRP). Despite the complexity of routing policies for service-function chains relative to traditional routing, AuditBox's protocol introduces 72-80% fewer bytes of overhead per packet (in a 5-hop service chain) and provides 61-67% higher goodput than prior work on VRPs designed for the Internet.

关键词： network function virtualization

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A Novel Layer Pruning Method Based on Graph Structure of Feature Representations for Specific Emitter Identification

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IEEE Transactions on Cognitive Communications and networking 2025年

作者： Yang, Ning Zhang, Bangning Ding, Guoru Guo, Daoxing Army Engineering University of PLA College of Communications and Engineering Nanjing210007 China

As an emerging method for physical layer security authentication, Specific Emitter Identification (SEI) plays a pivotal role in wireless communication network security. Leveraging its strong feature extraction capabilities, deep learning has exhibited excellent performance in SEI. However, most network architectures have many parameters, e.g., AlexNet, VGG, and ResNet. This brings a challenge for their deployment in devices especially those with constrained computation capacities. To address above concerns, this paper proposes a layer pruning method based on the graph structure of feature representations, which can simultaneously lightweight the neural network architecture, decrease the model complexity, and maintain the identification performance. This paper maps feature representations into a dynamic graph evolution process and determines redundant parameters based on the community detection of the graph. Moreover, the pruning process operates at the layer level, making it more convenient and not affecting the existing neural network training process. Experimental results demonstrate that the method can significantly reduce computational complexity, and the pruned models exhibit comparable or even better performance compared to the original models on multiple datasets. For instance, when compressing the computational cost of VGG16 by 95.4%, the identification accuracy is improved by 1.22%. © 2015 IEEE.

关键词： network function virtualization

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Multi-objective Optimization Service function Chain Placement Algorithm Based on Reinforcement Learning

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JOURNAL OF network AND SYSTEMS MANAGEMENT 2022年第4期30卷 58-58页

作者： Liu, Hongtai Ding, Shengduo Wang, Shunyi Zhao, Gang Wang, Chao CNPC Res Inst Safety & Environm Technol Beijing 102206 Peoples R China China Univ Petr East China Coll Comp Sci & Technol Qingdao 266580 Peoples R China

network function virtualization (NFV) makes the realization of specific network functions no longer depend on inherent hardware by executing virtual network functions (VNFs), but realizes network functions in a more flexible programming manner, thereby reducing the pressure of resource allocation on the underlying network. Service function chain (SFC) is composed of a set of fixed order VNFs. These VNFs need to be deployed on appropriate physical nodes to meet user function requirements, i.e., the placement of SFC. Traditional solutions mostly use mathematical models or heuristic methods, which are not applicable in the context of large-scale networks. Secondly, the existing methods do not integrate intelligent learning algorithms into the service function chain placement (SFCP) problem, which limits the possibility of obtaining better solutions. This paper presents a multi-objective optimization service function chain placement (MOO-SFCP) algorithm based on reinforcement learning (RL). The goal of the algorithm is to optimize the resource allocation mode, including several performance indexes such as underlying resource consumption revenue, revenue cost ratio, VNF acceptance rate and network latency. We model the SFCP as a Markov decision process (MDP), and use a two-layer policy network as an intelligent agent. In the training stage of RL, the agent comprehensively considers the optimization objectives and formulates the optimal physical node mapping strategy for VNF requests. In the test phase, the whole SFCP is completed according to the trained node mapping strategy. Simulation results show that the algorithm proposed in this paper has excellent performance in the aspects of underlying resource allocation revenue, VNF acceptance rate and so on. In addition, we prove that the algorithm has good flexibility by changing the delay constraint.

关键词： network function virtualization Virtual network function Service function chain placement Reinforcement learning Markov decision process

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EmcFIS: Evolutionary multi-criteria Fuzzy Inference System for virtual network function placement and routing

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APPLIED SOFT COMPUTING 2022年第0期117卷 108427-108427页

作者： Zahedi, Seyed Reza Jamali, Shahram Bayat, Peyman Islamic Azad Univ Rasht Branch Dept Comp Engn Rasht Iran Univ Mohaghegh Ardabili Dept Comp Engn Ardebil Iran

With the increasing demands for low-delay network services, mobile edge computing (MEC) has emerged as an appealing solution to provide computing resources in close to the end users. network function virtualization (NFV) is a new network architecture which replaces dedicated hardware middleboxes with software instances to run network functions via software virtualization on generalpurpose servers deployed at edge clouds. Because of the resource limitation at network edges, efficient placement and routing for online virtual network function requests (VNF-PRO) is a challenging task. The VNF-PRO has proven to be NP-hard, and thus, metaheuristic algorithms are the best choice in term of the solution quality. However, metaheuristics suffer from high computational complexity, and cannot be performed for online requests in the VNF-PRO. In this paper, a combined model based on fuzzy logic and genetic algorithm is proposed to achieve proper solution quality-speed trade-off in the VNF-PRO. In this method, a multi-criteria fuzzy inference system (named mcFIS) is used for the online VNF placement and routing. To achieve the best performance, a multi-objective evolutionary algorithm based on genetic algorithm (GA) is utilized in an offline procedure for automatic rule tuning of the mcFIS, once before applying it for online applications. Simulation results on two NFV benchmark instances demonstrate the efficiency of the proposed model against the existing techniques.(c) 2022 Elsevier B.V. All rights reserved.

关键词： network function virtualization Edge clouds Virtual network function Service function chain Fuzzy inference system Genetic algorithm

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