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10th International Conference on network and Service Management, CNSM 2014

作者： Bondan, Lucas Dos Santos, Carlos Raniery Paula D. Granville, Lisandro Zambenedetti Institute of Informatics Federal University of Rio Grande Do sul Av. Bento Gonçalves Porto Alegre9500 Brazil

ISBN: (纸本)9783901882661

network functions virtualization (NFV) is a new approach to design, deploy, and manage network functions. In a recent past, such functions used to be implemented at hardware. This approach, besides effective, presents many disadvantages such as increased operational costs, difficulties to scale up or down the network, and deploy new functions. The rise of virtualization technologies, on the other side, provides new ways to rethink about network functions. Instead of specialized and expensive hardware, multiple network functions can share the same commodity hardware, thus contributing to a better utilization of resources. Besides its advantages, NFV is still on its early stages of employment. Important aspects are not yet being investigated by the research community. For example, to this date, the management requirements of NFV remain unclear. Therefore, the present paper addresses this subject, it presents a realistic network function request, which is used to identify management requirements in the context of a specific NFV enabler platform called ClickOS. © 2014 IFIP.

关键词： network function virtualization

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Generalized virtual networking: An enabler for service centric networking and network function virtualization 16

Generalized virtual networking: An enabler for service centr...

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16th International Telecommunications network Strategy and Planning Symposium, networks 2014

作者： Salsano, Stefano Blefari-Melazzi, Nicola Presti, Francesco Lo Siracusano, Giuseppe Ventre, Pier Luigi University of Rome Tor Vergata Italy Consortium GARR Italy

ISBN: (纸本)9781479965151

In this paper we introduce the Generalized Virtual networking (GVN) concept. GVN provides a framework to influence the routing of packets based on service level information that is carried in the packets. It is based on a protocol header inserted between the network and Transport layers, therefore it can be seen as a layer 3.5 solution. Technically, GVN is proposed as a new transport layer protocol in the TCP/IP protocol suite. An IP router that is not GVN capable will simply process the IP destination address as usual. Similar concepts have been proposed in other works, and referred to as Service Oriented networking, Service Centric networking, Application Delivery networking, but they are now generalized in the proposed GVN framework. In this respect, the GVN header is a generic container that can be adapted to serve the needs of arbitrary service level routing solutions. The GVN header can be managed by GVN capable end-hosts and applications or can be pushed/popped at the edge of a GVN capable network (like a VLAN tag). In this position paper, we show that Generalized Virtual networking is a powerful enabler for SCN (Service Centric networking) and NFV (network function virtualization) and how it couples with the SDN (Software Defined networking) paradigm. © 2014 IEEE.

关键词： network function virtualization

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Optical traffic steering for network function virtualization in data centers

Optical traffic steering for network function virtualization...

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Photonic networks and Devices, 2014

作者： Xia, Ming Shirazipour, M. Zhang, Y. Green, H. Takacs, A. Ericsson Inc. United States

This talk will discuss the challenges of steering large flows across multiple virtualized network functions in data centers, and introduce a novel architecture to steer flows in both optical and packet domains for hig... 详细信息

ISBN: (纸本)9781557527370

关键词： network function virtualization

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Multisite gaming streaming optimization over virtualized 5G environment using Deep Reinforcement Learning techniques

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COMPUTER networkS 2024年 244卷

作者： del Rio, Alberto Serrano, Javier Jimenez, David Contreras, Luis M. Alvarez, Federico Univ Politecn Madrid GATV Res Grp Madrid 28040 Spain Telefonica ID Global CTIO Unit Madrid 28050 Spain

The massive growth of live streaming, especially gaming -focused content, has led to an overall increase in global bandwidth consumption. Certain services see their quality diminished at times of peak consumption, degrading the quality of the content. This trend generates new research related to optimizing image quality according to network and service conditions. In this work we present a gaming streaming use case optimization on a real multisite 5G environment. The paper outlines the virtualized workflow of the use case and provides a detailed description of the applications and resources deployed for the simulation. This simulation tests the optimization of the service based on the addition of Artificial Intelligence (AI) algorithms, assuring the delivery of content with good Quality of Experience (QoE) under different working conditions. The AI introduced is based on Deep Reinforcement Learning (DRL) algorithms that can adapt, in a flexible way, to the different conditions that the multimedia workflow could face. That is, adapt, through corrective actions, the streaming bitrate, in order to optimize the QoE of the content on a real-time multisite scenario. The results of this work demonstrate how we have been able to minimize content losses, as well as the fact of obtaining high audiovisual multimedia quality results with higher bitrates, compared to a service without an optimizer integrated in the system. In a multi -site environment, we have achieved an improvement of 20 percentage points in terms of blockiness efficiency and also 15 percentage points in block loss.

关键词： Deep Reinforcement Learning Multiaccess edge computing Software-defined networks 5G network function virtualization Adaptive multimedia A3C Quality of Experience

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Parallel Placement of Virtualized network functions via Federated Deep Reinforcement Learning

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IEEE-ACM TRANSACTIONS ON networkING 2024年第4期32卷 2936-2949页

作者： Huang, Haojun Tian, Jialin Min, Geyong Yin, Hao Zeng, Cheng Zhao, Yangming Wu, Dapeng Oliver Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan 430074 Peoples R China Univ Exeter Dept Comp Sci Exeter EX4 4QF England Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol BNRist Beijing 100084 Peoples R China Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230026 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

network function virtualization (NFV) introduces a new network architecture that offers different network services flexibly and dynamically in the form of Service function Chains (SFCs), which refer to a set of virtualization network functions (VNFs) chained in a specific order. However, the service latency often increases linearly with the length of SFCs due to the sequential execution of VNFs, resulting in sub-optimal performance for most delay-sensitive applications. In this paper, a novel Parallel VNF Placement (PVFP) approach is proposed for real-world networks via Federated Deep Reinforcement Learning (FDRL). PVFP has three remarkable characteristics distinguishing from previous work: 1) PVFP designs a specific parallel principle, with three parallelism identification rules, to reasonably decide partial VNF parallelism;2) PVFP considers SFC partition in multi-domains built on their remaining resources and potential parallel VNFs to ensure that VNFs can be reasonably distributed for resource balancing among domains;3) FDRL-based framework of parallel VNF placement is designed to train a global intelligent model, with time-variant local autonomy explorations, for cross-domain SFC deployment, avoiding data sharing among domains. Simulation results in different scenarios demonstrate that PVFP can significantly reduce the end-to-end latency of SFCs at the medium resource expenditures to place VNFs in multiple administrative domains, compared with the state-of-the-art mechanisms.

关键词： Deep reinforcement learning network function virtualization parallel placement deep reinforcement learning multiple domains

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Resource allocation for UAV-enabled multi-access edge computing

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JOURNAL OF SUPERCOMPUTING 2024年第15期80卷 22770-22802页

作者： Falcao, Marcos Souza, Caio Bruno Balieiro, Andson Dias, Kelvin Univ Fed Pernambuco Ctr Informat CIn Ave Jornalista Anibal Fernandes BR-50740560 Recife PE Brazil

In Ultrareliable and Low Latency Communications (URLLC), balancing trade-offs between energy consumption, service availability, and strict reliability and latency requirements is a significant challenge, especially in unmanned aerial vehicle (UAV)-enabled multi-access edge computing (MEC) environments. The constraints imposed by the size, weight and power limitations of UAVs further complicate this task. This study addresses optimizing resource allocation in such environments to meet URLLC demands while minimizing power consumption and maximizing service availability. We explore the virtualization layer of the network function virtualization (NFV)-MEC architecture, incorporating node availability and power consumption alongside conflicting URLLC reliability and latency demands. We introduce an energy-aware model based on continuous-time Markov chain (CTMC) with an embedded virtual resource scaling scheme for Dynamic Resource Allocation (DRA). To solve the optimization problem related to MEC-enabled UAV node dimensioning, we propose a genetic algorithm (GA)-based solution. Our results demonstrate that the proposed GA-based approach achieves a superior balance, with up to a 44% reduction in power consumption compared to the first fit with maximum resources strategy, while also improving service availability and meeting URLLC requirements. This work provides a comprehensive analysis of key virtualization parameters and their impact on critical services within a single NFV-MEC over a UAV node, offering a robust framework for future 6 G network applications.

关键词： Multi-access edge computing Ultrareliable and low latency communications Continuous-time Markov chains network function virtualization Unmanned aerial vehicles

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Dynamic resource allocation for URLLC and eMBB in MEC-NFV 5G networks

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COMPUTER networkS 2025年 260卷

作者： Souza, Caio Falcao, Marcos Balieiro, Andson Alves, Elton Taleb, Tarik Univ Fed Pernambuco Ctr Informat CIN Recife PE Brazil Univ Sul & Sudeste Para Falcudade Computacao Maraba Brazil Ruhr Univ Bochum Fac Elect Engn & Informat Technol Bochum Germany

Supporting the coexistence between enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communications (URLLC) is a major challenge in modern communication systems due to their diverse requirements. Multi-access Edge Computing (MEC), network function virtualization (NFV), and network Slicing (NS) emerge as complementary paradigms to address this challenge, providing fine-grained, on-demand resources closer to the User Equipment (UE) and enabling shared utilization of physical infrastructure. This paper addresses the combination of MEC, NFV, NS, and dynamic virtual resource allocation for overcoming the problem of resource dimensioning at the network edge supporting eMBB and URLLC services. We have proposed a Continuous-Time Markov Chain (CTMC) model to evaluate how requests are managed by the virtualization resources of a single MEC node, primarily focusing on fulfilling the requirements of both eMBB and URLLC services. It characterizes the dynamic virtual resource allocation process and incorporates three key performance metrics, relevant for both URLLC and eMBB services (e.g., availability and response time) as well as for service providers (e.g., power consumption). The model also integrates practical factors such as failures during service processing, service prioritization, and setup (repair) times, enabling insights into how the MEC-NFV-based 5G network handles different service categories by applying service prioritization and dynamic resource allocation. Our key findings reveal that container setup and failure rates playa crucial role in both availability and response times, higher setup rates improve availability and shorten response times. Additionally, the number of containers significantly enhances both metrics, whereas buffer sizes primarily influence response times. Furthermore, higher eMBB arrival rates reduce availability and increase response times, while URLLC availability remains unaffected.

关键词： Multi-access Edge Computing Ultra-Reliable Low-Latency Communications Continuous-Time Markov Chain network function virtualization Enhanced Mobile Broadband Dynamic resource allocation

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Hybrid Serverless Platform for Smart Deployment of Service function Chains

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IEEE TRANSACTIONS ON CLOUD COMPUTING 2025年第1期13卷 351-368页

作者： Sheshadri, K. R. Lakshmi, J. Indian Inst Sci Cloud Syst Lab Bangalore 560012 India

Cloud Data Centres deal with dynamic changes all the time. networks in particular, need to adapt their configurations to changing workloads. Given these expectations, network function virtualization (NFV) using Software Defined networks (SDNs) has realized the aspect of programmability in networks. NFVs allow network services to be programmed as software entities that can be deployed on commodity clusters in the Cloud. Being software, they inherently carry the ability to be customized to specific tenants' requirements and thus support multi-tenant variations with ease. However, the ability to exploit scaling in alignment with changing demands with minimal loss of service, and improving resource usage efficiency still remains a challenge. Several recent works in literature have proposed platforms to realize Virtual network functions (VNFs) on the Cloud using service offerings such as Infrastructure as a Service (IaaS) and serverless computing. These approaches are limited by deployment difficulties (configuration and sizing), adaptability to performance requirements (elastic scaling), and changing workload dynamics (scaling and customization). In the current work, we propose a Hybrid Serverless Platform (HSP) to address these identified lacunae. The HSP is implemented using a combination of persistent IaaS, and FaaS components. The IaaS components handle the steady state load, whereas the FaaS components activate during the dynamic change associated with scaling to minimize service loss. The HSP controller takes provisioning decisions based on Quality of Service (QoS) rules and flow statistics using an auto recommender, alleviating users of sizing decisions for function deployment. HSP controller design exploits data locality in SFC realization, reducing data-transfer times between VNFs. It also enables the usage of application characteristics to offer higher control over SFC deployment. A proof-of-concept realization of HSP is presented in the paper and is evaluated

关键词： FaaS network function virtualization serverless computing serverless computing serverless computing

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Assembling multipath service function chains in substrate graphs using sharing instances and deep learning

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 152卷

作者： Chen, Zhanwei Rezaeipanah, Amin Zhoukou Normal Univ Sch Comp Sci & Technol Zhoukou 466001 Henan Peoples R China Univ Rahjuyan Danesh Borazjan Dept Comp Engn Bushehr Iran

The assembly of Service function Chains (SFCs) within substrate graphs has emerged as a crucial challenge in the context of network function virtualization. SFCs are ordered sequences of Virtual network functions (VNFs) that must be executed to meet specific service requirements. Efficient management of network resources in this domain can be achieved through the reuse of sharable VNF instances, which reduces resource consumption and operational overhead. As network environments evolve with increasing demands and dynamic complexities, multipath SFCs have gained attention as a promising approach to enhance fault tolerance and optimize resource utilization. Multipath SFCs allow traffic to traverse multiple distinct paths in the network, ensuring load balancing, redundancy, and improved performance. With this motivation, this study presents a multipath SFC assembly framework via VNF instance sharing in substrate graphs, implemented using deep learning algorithms. The proposed algorithm leverages the Soft Actor-Critic algorithm to embed multipath SFCs effectively. By dynamically mapping SFCs to paths with minimal bandwidth consumption and latency, our algorithm optimizes network resource usage. The resource allocation mechanism in proposed algorithm is designed to adapt flexibly to diverse request demands. Moreover, by utilizing reusable VNF instances, our algorithm further enhances resource efficiency. Also, the framework ensures that all nodes and links in the physical network satisfy resource constraints during the mapping process. Simulation results demonstrate that proposed algorithm significantly improves performance metrics under multipath SFC assembly with instance sharing in substrate graphs. Specifically, proposed algorithm outperforms state-of-the-art methods by improving bandwidth consumption by 1.04 %, reducing latency by 0.82ms, and increasing the SFC assembly rate by 2.75 %, demonstrating efficient network resource management.

关键词： network function virtualization Service function chains Sharing instances Substrate graphs Deep learning

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Reliability-Aware Resource Allocation for SFC: A Column Generation-Based Link Protection Approach

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IEEE TRANSACTIONS ON network AND SERVICE MANAGEMENT 2024年第4期21卷 4583-4597页

作者： Li, Wenqian Qu, Long Liu, Juan Xie, Lingfu Ningbo Univ Fac Elect Engn & Comp Sci Ningbo 315211 Peoples R China

network function virtualization (NFV) is considered one of the key technologies of 5G/B5G because of its advantages of flexibility, scalability, and manageability. In NFV networks, the flow of network service needs to go through a certain number of Virtual network functions (VNFs) which form Service function Chain (SFC). Compared to link protection in traditional networks, the backup transmission links for different types of VNFs need to be considered to improve the SFCs' reliability, since any failure of transmission link may interrupt the network service. Due to the uncertainty of VNF placement and routing, the flexible selection of link backup for each VNF to satisfy the reliability requirement of SFC becomes a remarkably challenging problem. In this paper, a Flexible virtual Link Protection (Fle_LP) mechanism is proposed to calculate backup resources accurately, enhancing the reliability of NFV-enabled network service. We mathematically formulate the problem as a Mixed Integer Nonlinear Program (MINLP). An Extended Least Square (ELS) method is introduced to deal with the nonlinear constraints, which transforms MINLP to Mixed Integer Linear Programming (MILP). Owing to the MILP's remarkable complexity, a Column Generation-based Link Protection (CG_LP) algorithm is proposed, which generates an acceptable sub-optimal solution. Numerical results show that CG_LP reduces the computing time (8-node network: 92.3%, 16-node network: 99.6%) while achieving the same bandwidth consumption as MILP.

关键词： Reliability Protection Surgery Delays 5G mobile communication Virtual links Resource management network function virtualization service function chain least square network reliability column generation

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