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13th International Conference on Experience of Designing and Application of CAD Systems in Microelectronics (CADSM)

作者： Skulysh, Mariia Klimovych, Oleg Natl Tech Univ Ukraine Kyiv Polytech Inst Inst Telecommun Syst Kiev Ukraine

ISBN: (纸本)9786176077169

In this paper the main aspects of usage network functions virtualization (NFV) in the domain of Evolved Packet Core (EPC) virtualization are given. Modern virtualization technologies in combination with existing tools for storage and computing, ensure virtualization and abstraction for the entire set of critical resources. The general aim of NFV is to deliver functions, networks and infrastructure as services rather than as features of vertically integrated systems. virtualization of EPC components using NFV approach can provide capacity support for mobile broadband services with high level of performance. For operators, the return on investment associated with migrating network services to the cloud comes in the form of a high degree of automation in network operation.

关键词： network functions virtualization Evolved Packet Core virtualization virtualized network functions

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Security-Aware Cost Optimized Dynamic Service Function Chain Scheduling

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JOURNAL OF network AND SYSTEMS MANAGEMENT 2025年第1期33卷 1-39页

作者： Dubba, Sudha Killi, Balaprakasa Rao Natl Inst Technol Dept Comp Sci & Engn Warangal 506003 Telangana India

The idea of network function virtualization has emerged recently as a means of accelerating the deployment of middleboxes and network operations while also lowering deployment costs. Service function chaining provides network connectivity and steers traffic between the deployed VNF instances to provide a network service. However, integrating NFV within SFC scheduling introduces complexities, particularly in efficiently allocating resources to VNFs amidst dynamic network traffic and service demands. Optimization of VNF placement and scheduling is essential to minimize execution costs while meeting stringent Service Level Agreements (SLAs) and ensuring quality-guaranteed services. Moreover, the convergence of NFV and SFC scheduling brings for security challenges, including unauthorized access, data interception, and service disruption. Balancing optimization objectives with stringent security requirements poses a non-trivial task, emphasizing the need for prioritizing security in resource allocation and scheduling decisions. Meeting SFC deadlines is challenging due to dynamic network conditions, service demands, and resource allocation complexity. Failure to meet deadlines can lead to service quality degradation, SLA violations, and financial penalties. To address these challenges, the security and cost-aware SFC scheduling problem is formulated as an optimization problem. Moreover, a three-level security model is designed for both the VNFs and the physical machines in the NFV-enabled networks. Since the problem is NP-hard, we propose two heuristics named Particle Swarm Optimization-Based SFC Scheduling approach and Group Learning Particle Swarm Optimization-Based SFC Scheduling model that focus on optimizing the execution cost of SFC while meeting security and deadline requirements. The proposed models are compared with the existing SFC scheduling models. The effectiveness of the proposed scheduling approaches is evaluated through extensive simulations and it is show

关键词： network functions virtualization Service function chain scheduling Virtual network function Dynamic scheduling Security Cost PSO service level agreement

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Priority-Aware Resource Allocation for VNF Deployment in Service Function Chains Based on Graph Reinforcement Learning

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Computers, Materials & Continua 2025年第5期83卷 1649-1665页

作者： Seyha Ros Seungwoo Kang Taikuong Iv Inseok Song Prohim Tam Seokhoon Kim Department of Software Convergence Soonchunhyang UniversityAsan31538Republic of Korea School of Digital Technologies American University of Phnom PenhPhnom Penh12106Cambodia Department of Computer Software Engineering Soonchunhyang UniversityAsan31538Republic of Korea

Recently,network functions virtualization(NFV)has become a critical resource for optimizing capability utilization in the 5G/B5G *** decomposes the network resource paradigm,demonstrating the efficient utilization of network functions(NFs)to enable configurable service priorities and resource *** Service Providers(TSPs)face challenges in network utilization,as the vast amounts of data generated by the Internet of Things(IoT)overwhelm existing *** applications,which generate massive volumes of diverse data and require real-time communication,contribute to bottlenecks and *** this context,Multiaccess Edge Computing(MEC)is employed to support resource and priority-aware IoT applications by implementing Virtual network Function(VNF)sequences within Service Function Chaining(SFC).This paper proposes the use of Deep Reinforcement Learning(DRL)combined with Graph Neural networks(GNN)to enhance network processing,performance,and resource pooling *** facilitates feature extraction through Message-Passing Neural network(MPNN)*** with DRL,Deep Q-networks(DQN)are utilized to dynamically allocate resources based on IoT network priorities and *** focus is on minimizing delay times for VNF instance execution,ensuring effective resource placement,and allocation in SFC deployments,offering flexibility to adapt to real-time changes in priority and *** results demonstrate that our proposed scheme outperforms reference models in terms of reward,delay,delivery,service drop ratios,and average completion ratios,proving its potential for IoT applications.

关键词： Deep reinforcement learning graph neural network multi-access edge computing network functions virtualization software-defined networking

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Towards a Cost Optimal Design for a 5G Mobile Core network Based on SDN and NFV

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IEEE TRANSACTIONS ON network AND SERVICE MANAGEMENT 2017年第4期14卷 1061-1075页

作者： Basta, Arsany Blenk, Andreas Hoffmann, Klaus Morper, Hans Jochen Hoffmann, Marco Kellerer, Wolfgang Tech Univ Munich Chair Commun Networks Munich Germany Nokia Bell Labs Res Dept Munich Germany Nokia Bell Labs Munich Germany Tech Univ Munich Dept Elect & Comp Engn Chair Commun Networks Munich Germany

With the rapid growth of user traffic, service innovation, and the persistent necessity to reduce costs, today's mobile operators are faced with several challenges. In networking, two concepts have emerged aiming at cost reduction, increase of network scalability and deployment flexibility, namely network functions virtualization (NFV) and Software Defined networking (SDN). NFV mitigates the dependency on hardware, where mobile network functions are deployed as software virtual network functions on commodity servers at cloud infrastructure, i.e., data centers. SDN provides a programmable and flexible network control by decoupling the mobile network functions into control plane and data plane functions. The design of the next generation mobile network (5G) requires new planning and dimensioning models to achieve a cost optimal design that supports a wide range of traffic demands. We propose three optimization models that aim at minimizing the network load cost as well as data center resources cost by finding the optimal placement of the data centers as well the SDN and NFV mobile network functions. The optimization solutions demonstrate the trade-offs between the different data center deployments, i.e., centralized or distributed, and the different cost factors, i.e., optimal network load cost or data center resources cost. We propose a Pareto optimal multi-objective model that achieves a balance between network and data center cost. Additionally, we use prior inference, based on the solutions of the single objectives, to pre-select data center locations for the multi-objective model that results in reducing the optimization complexity and achieves savings in run time while keeping a minimal optimality gap.

关键词： Software Defined networking network functions virtualization 5G mobile core network optimization

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Software-Defined Architectures and Technologies for Underwater Wireless Sensor networks: A Survey

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IEEE COMMUNICATIONS SURVEYS AND TUTORIALS 2018年第4期20卷 2855-2888页

作者： Luo, Hanjiang Wu, Kaishun Ruby, Rukhsana Liang, Yongquan Guo, Zhongwen Ni, Lionel M. Shandong Univ Sci & Technol Sch Comp Sci & Engn Qingdao 266510 Peoples R China Shenzhen Univ Dept Comp Sci & Engn Shenzhen 518060 Peoples R China Ocean Univ China Dept Comp Sci Qingdao 266100 Peoples R China Univ Macau Dept Comp & Informat Sci Macau Peoples R China

The ocean covers nearly two-thirds of the surface on the Earth, and there has been great interest in developing underwater wireless sensor networks (UWSNs) to help us explore the ocean realm. A great deal of efforts have been devoted to it, and significant progress has been made since the beginning of 2000s. However, most of the networks are isolatedly developed currently, inherently hardware-based and application-oriented with inflexible closed-form architectures, which are difficult to reconfigure, reprogram and evolve. They also lack the capability in sharing resources, and are far from service-oriented networks. These limitations impair their capacity for wide range of applications. To further propel the development of UWSNs, next-generation UWSNs have been proposed recently, which are robust, flexible, adaptive, programmable, support resource-sharing feature and are easy to manage and evolve. Moreover, a number of novel software-defined techniques and paradigms, such as software-defined radio, cognitive acoustic radio, network function virtualization, software-defined networking, Internet of Underwater Things, and sensor-cloud, have been emerging. These software-defined technologies have the capability of softwarizing network resources, and then redefining them to satisfy diverse application requirements, improve resource utilization efficiency and simplify network management. Consequently, these evolving technologies are envisioned as critical building blocks and major driving forces, which will transform conventional UWSNs toward software-based, programmable, user-customizable, and service-oriented next-generation UWSNs. In this paper, we provide a comprehensive review of existing works on implementing these techniques, and also present discussions for future research. We hope to inspire more active research on these areas and take a step further toward realizing next-generation UWSNs.

关键词： Underwater sensor networks software-defined networking software-defined radio software-defined acoustic modem software-defined cognitive network network functions virtualization

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Multi-Agent Deep Q-networks for Efficient Edge Federated Learning Communications in Software-Defined IoT

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Computers, Materials & Continua 2022年第5期71卷 3319-3335页

作者： Prohim Tam Sa Math Ahyoung Lee Seokhoon Kim Department of Software Convergence Soonchunhyang UniversityAsan31538Korea Department of Computer Science Kennesaw State UniversityMariettaGA 30060USA Department of Computer Software Engineering Soonchunhyang UniversityAsan31538Korea

Federated learning(FL)activates distributed on-device computation techniques to model a better algorithm performance with the interaction of local model updates and global model distributions in aggregation averaging ***,in large-scale heterogeneous Internet of Things(IoT)cellular networks,massive multi-dimensional model update iterations and resource-constrained computation are challenging aspects to be tackled *** paper introduces the system model of converging softwaredefined networking(SDN)and network functions virtualization(NFV)to enable device/resource abstractions and provide NFV-enabled edge FL(eFL)aggregation servers for advancing automation and ***-agent deep Q-networks(MADQNs)target to enforce a self-learning softwarization,optimize resource allocation policies,and advocate computation offloading *** gathered network conditions and resource states,the proposed agent aims to explore various actions for estimating expected longterm rewards in a particular state *** exploration phase,optimal actions for joint resource allocation and offloading decisions in different possible states are obtained by maximum Q-value ***-based virtual network functions(VNF)forwarding graph(VNFFG)is orchestrated to map VNFs towards eFL aggregation server with sufficient communication and computation resources in NFV infrastructure(NFVI).The proposed scheme indicates deficient allocation actions,modifies the VNF backup instances,and reallocates the virtual resource for exploitation *** neural network(DNN)is used as a value function approximator,and epsilongreedy algorithm balances exploration and *** scheme primarily considers the criticalities of FL model services and congestion states to optimize long-term *** results presented the outperformance of the proposed scheme over reference schemes in terms of Quality of Service(QoS)performance metrics,including packet

关键词： Deep Q-networks federated learning network functions virtualization quality of service software-defined networking

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FlexState: Flexible State Management of network functions

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IEEE ACCESS 2021年 9卷 46837-46850页

作者： Pozza, Matteo Rao, Ashwin Lugones, Diego F. Tarkoma, Sasu Univ Helsinki Dept Comp Sci Helsinki 00014 Finland Nokia Bell Labs Dublin D15 Y6NT 15 Ireland

network function (NF) developers have traditionally prioritized performance when creating new packet processing capabilities. This was usually driven by a market demand for highly available solutions with differentiating features running at line rate, even at the expense of flexibility and tightly-coupled monolithic designs. Today, however, the market advantage is achieved by providing more features in shorter development cycles and quickly deploying them in different operating environments. In fact, network operators are increasingly adopting continuous software delivery practices as well as new architectural styles (e.g., microservices) to decouple functionality and accelerate development. A key challenge in revisiting NF design is state management, which is usually highly optimized for a deployment by carefully selecting the underlying data store. Therefore, migrating to a data store that suits a different use case is time-consuming as it requires code refactoring and adaptation to new application programming interfaces, APIs. As a result, refactoring NF software for different environments can take up to months, reducing the pace at which new features and upgrades can be deployed in production networks. In this paper, we demonstrate experimentally that it is feasible to introduce an abstraction layer to decouple NF state management from the data store adopted while still approaching line-rate performance. We present FlexState, a state management system that exposes data store functionality as configuration options, which reduces code refactoring efforts. Experiments show that FlexState achieves significant flexibility in optimizing the state management, and accelerates deployment on new scenarios while preserving performance and scalability.

关键词： Noise measurement Scalability Production Reliability engineering Distributed databases Optimization Middleware Data storage systems middleboxes network functions network functions virtualization NFV parallel processing state management

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Statelet-Based Efficient and Seamless NFV State Transfer

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IEEE TRANSACTIONS ON network AND SERVICE MANAGEMENT 2017年第4期14卷 964-977页

作者： Nobach, Leonhard Rimac, Ivica Hilt, Volker Hausheer, David Tech Univ Darmstadt Peer To Peer Syst Engn Lab D-64283 Darmstadt Germany Nokia Bell Labs Autonomous Software Syst Res Grp D-70435 Stuttgart Germany Tech Univ Darmstadt Dept Elect Engn & Informat Technol Darmstadt Germany

network functions virtualization (NFV) environments can provide increased elasticity and flexibility for operators, as network-related services can be scaled and moved as needed. Such operations require a seamless transfer of state to provide a service without interruption or performance degradation. In this paper, we propose and analyze a novel approach for efficient and seamless NFV state transfers. Our approach is based on the concept of statelets, which are compact representations of information in incoming packets that change the state of a virtualized network function. We present and describe slim migration (SliM), a system for efficient NFV state transfers using a statelet interface that we have implemented as an add-on to the data plane development kit, a high-performance packet I/O library. We have evaluated SliM in a testbed and present results that show its benefits in terms of lower delays and lower packet-loss rates. Moreover, our analysis and evaluation results show that SliM offers three times as much shared link capacity to the dataplane as previous approaches, while reducing the duration of a state transfer to one-third of their time.

关键词： network functions virtualization state migration state management virtualization sofware-defined networking cost-efficiency

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A Simulation Framework for Virtualized Resources in Cloud Data Center networks

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2019年第8期37卷 1808-1819页

作者： Bellavista, Paolo Corradi, Antonio Foschini, Luca Luciano, Sabato Solimando, Michele Univ Bologna DISI I-40136 Bologna Italy

Many IT companies are embracing the new softwarization paradigm through the adoption of new architecture models, such as software-defined network and network function virtualization, primarily to limit the costs of maintaining and deploying their network infrastructures, by giving the possibility to service/application providers to reconfigure and programmatically perform actions on the network. Accordingly, the dynamic management of the data center networks requires complex operations to ensure high availability and continuous reliability in order to guarantee full functionality of the virtualized resources. In this context, simulator-based approaches are helpful for planning and evaluating the deployment of the cloud data center networking, but existing cloud simulators have several limitations: they have too high overhead for wide-scale data center networks, complex configuration, and too abstract deployment models. For these motivations, we propose DCNs-2, a novel extension for the Ns-2 simulator, as a valid solution to efficiently simulate a cloud network infrastructure, with all the involved entities, such as switches, physical/virtual machines, and racks. The proposed solution not only makes configuration easier, but through extensive tests, we show that its execution overhead is limited to less than 130 MB of memory and the execution time is acceptable even for very wide-scale and complex deployment environments.

关键词： network simulation data center network software-defined networks network functions virtualization cloud computing infrastructure as a service Ns-2

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Online Stochastic Buy-Sell Mechanism for VNF Chains in the NFV Market

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2017年第2期35卷 392-406页

作者： Zhang, Xiaoxi Huang, Zhiyi Wu, Chuan Li, Zongpeng Lau, Francis C. M. Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China Univ Calgary Dept Comp Sci Calgary AB T2N 1N4 Canada

With the recent advent of network functions virtualization (NFV), enterprises and businesses are looking into network service provisioning through the service chains of virtual network functions (VNFs), instead of relying on dedicated hardware middleboxes. Accompanying this trend, an NFV market is emerging, where NFV service providers create VNF instances, assemble VNF service chains, and sell them for the use of customers, using resources (computing, bandwidth) that they own or rent from other resource suppliers. Efficient service chain provisioning and pricing mechanisms are still missing, to charge assembled service chains according to demand and the supply of resources at any time. We propose an online stochastic auction mechanism for on-demand service chain provisioning and pricing at an NFV provider. Our auction takes in buy bids for service chains from multiple customers and sell bids from various resource suppliers to supplement the NFV provider's geo-distributed resource pool, with resource occupation/contribution durations. We extend online primal-dual optimization framework for handling both buyers and sellers, with a new competitive analysis. The online mechanism maximizes the expected social welfare of the NFV ecosystem (the NFV provider, customers and resource suppliers) with a good competitive ratio as compared with the expected offline optimal social welfare, while guaranteeing truthfulness in bidding, individual rationality for both buyers and sellers, and polynomial time for computation. We evaluate our mechanism through trace-driven simulation studies, and demonstrate a close-to-offline-optimal performance in expected social welfare under realistic settings.

关键词： Auction mechanism design online algorithms network functions virtualization

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