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Journal articles on the topic 'Placement des VNF'

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Published: 20 July 2024

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1

Sharma, Gourav Prateek, Wouter Tavernier, Didier Colle, and Mario Pickavet. "VNF-AAPC: Accelerator-aware VNF placement and chaining." Computer Networks 177 (August 2020): 107329. http://dx.doi.org/10.1016/j.comnet.2020.107329.

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2

Battisti, Anselmo Luiz Éden, Evandro Luiz Cardoso Macedo, Marina Ivanov Pereira Josué, Hugo Barbalho, Flávia C. Delicato, Débora Christina Muchaluat-Saade, Paulo F. Pires, Douglas Paulo de Mattos, and Ana Cristina Bernardo de Oliveira. "A Novel Strategy for VNF Placement in Edge Computing Environments." Future Internet 14, no. 12 (November 30, 2022): 361. http://dx.doi.org/10.3390/fi14120361.

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Network function virtualization (NFV) is a novel technology that virtualizes computing, network, and storage resources to decouple the network functions from the underlying hardware, thus allowing the software implementation of such functions to run on commodity hardware. By doing this, NFV provides the necessary flexibility to enable agile, cost-effective, and on-demand service delivery models combined with automated management. Different management and orchestration challenges arise in such virtualized and distributed environments. A major challenge in the selection of the most suitable edge nodes is that of deploying virtual network functions (VNFs) to meet requests from multiple users. This article addresses the VNF placement problem by providing a novel integer linear programming (ILP) optimization model and a novel VNF placement algorithm. In our definition, the multi-objective optimization problem aims to (i) minimize the energy consumption in the edge nodes; (ii) minimize the total latency; and (iii) reducing the total cost of the infrastructure. Our new solution formulates the VNF placement problem by taking these three objectives into account simultaneously. In addition, the novel VNF placement algorithm leverages VNF sharing, which reuses VNF instances already placed to potentially reduce computational resource usage. Such a feature is still little explored in the community. Through simulation, numerical results show that our approach can perform better than other approaches found in the literature regarding resource consumption and the number of SFC requests met.

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3

Wu, Xing, Jing Duan, Mingyu Zhong, Peng Li, and Jianjia Wang. "VNF Chain Placement for Large Scale IoT of Intelligent Transportation." Sensors 20, no. 14 (July 8, 2020): 3819. http://dx.doi.org/10.3390/s20143819.

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With the advent of the Internet of things (IoT), intelligent transportation has evolved over time to improve traffic safety and efficiency as well as to reduce congestion and environmental pollution. However, there are some challenging issues to be addressed so that it can be implemented to its full potential. The major challenge in intelligent transportation is that vehicles and pedestrians, as the main types of edge nodes in IoT infrastructure, are on the constant move. Hence, the topology of the large scale network is changing rapidly over time and the service chain may need reestablishment frequently. Existing Virtual Network Function (VNF) chain placement methods are mostly good at static network topology and any evolvement of the network requires global computation, which leads to the inefficiency in computing and the waste of resources. Mapping the network topology to a graph, we propose a novel VNF placement method called BVCP (Border VNF Chain Placement) to address this problem by elaborately dividing the graph into multiple subgraphs and fully exploiting border hypervisors. Experimental results show that BVCP outperforms the state-of-the-art method in VNF chain placement, which is highly efficient in large scale IoT of intelligent transportation.

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4

Umrao, Brajesh Kumar, and Dharmendar Kumar Yadav. "APVNFC: Adaptive Placement of Virtual Network Function Chains." Cybernetics and Information Technologies 23, no. 1 (March 1, 2023): 59–74. http://dx.doi.org/10.2478/cait-2023-0003.

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Abstract Designing efficient and flexible approaches for placement of Virtual Network Function (VNF) chains is the main success of Network Function Virtualization (NFV). However, most current work considers the constant bandwidth and flow processing requirements while deploying the VNFs in the network. The constant (immutable) flow processing and bandwidth requirements become critical limitations in an NFV-enabled network with highly dynamic traffic flow. Therefore, bandwidth requirements and available resources of the Point-of-Presence (PoP) in the network change constantly. We present an adaptive model for placing VNF chains to overcome this limitation. At the same time, the proposed model minimizes the number of changes (i.e., re-allocation of VNFs) in the network. The experimental evaluation shows that the adaptive model can deliver stable network services. Moreover, it reduces the significant number of changes in the network and ensures flow performance.

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5

Abdelaal, Marwa A., Gamal A. Ebrahim, and Wagdy R. Anis. "Efficient Placement of Service Function Chains in Cloud Computing Environments." Electronics 10, no. 3 (January 30, 2021): 323. http://dx.doi.org/10.3390/electronics10030323.

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The widespread adoption of network function virtualization (NFV) leads to providing network services through a chain of virtual network functions (VNFs). This architecture is called service function chain (SFC), which can be hosted on top of commodity servers and switches located at the cloud. Meanwhile, software-defined networking (SDN) can be utilized to manage VNFs to handle traffic flows through SFC. One of the most critical issues that needs to be addressed in NFV is VNF placement that optimizes physical link bandwidth consumption. Moreover, deploying SFCs enables service providers to consider different goals, such as minimizing the overall cost and service response time. In this paper, a novel approach for the VNF placement problem for SFCs, called virtual network functions and their replica placement (VNFRP), is introduced. It tries to achieve load balancing over the core links while considering multiple resource constraints. Hence, the VNF placement problem is first formulated as an integer linear programming (ILP) optimization problem, aiming to minimize link bandwidth consumption, energy consumption, and SFC placement cost. Then, a heuristic algorithm is proposed to find a near-optimal solution for this optimization problem. Simulation studies are conducted to evaluate the performance of the proposed approach. The simulation results show that VNFRP can significantly improve load balancing by 80% when the number of replicas is increased. Additionally, VNFRP provides more than a 54% reduction in network energy consumption. Furthermore, it can efficiently reduce the SFC placement cost by more than 67%. Moreover, with the advantages of a fast response time and rapid convergence, VNFRP can be considered as a scalable solution for large networking environments.

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6

Tao, Xiaoyi, Kaoru Ota, Mianxiong Dong, Heng Qi, and Keqiu Li. "Cost as Performance: VNF Placement at the Edge." IEEE Networking Letters 3, no. 2 (June 2021): 70–74. http://dx.doi.org/10.1109/lnet.2021.3065651.

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7

Leivadeas, Aris, George Kesidis, Mohamed Ibnkahla, and Ioannis Lambadaris. "VNF Placement Optimization at the Edge and Cloud †." Future Internet 11, no. 3 (March 9, 2019): 69. http://dx.doi.org/10.3390/fi11030069.

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Network Function Virtualization (NFV) has revolutionized the way network services are offered to end users. Individual network functions are decoupled from expensive and dedicated middleboxes and are now provided as software-based virtualized entities called Virtualized Network Functions (VNFs). NFV is often complemented with the Cloud Computing paradigm to provide networking functions to enterprise customers and end-users remote from their premises. NFV along with Cloud Computing has also started to be seen in Internet of Things (IoT) platforms as a means to provide networking functions to the IoT traffic. The intermix of IoT, NFV, and Cloud technologies, however, is still in its infancy creating a rich and open future research area. To this end, in this paper, we propose a novel approach to facilitate the placement and deployment of service chained VNFs in a network cloud infrastructure that can be extended using the Mobile Edge Computing (MEC) infrastructure for accommodating mission critical and delay sensitive traffic. Our aim is to minimize the end-to-end communication delay while keeping the overall deployment cost to minimum. Results reveal that the proposed approach can significantly reduce the delay experienced, while satisfying the Service Providers’ goal of low deployment costs.

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8

Tahmasbi Nejad, Mohammad Ali, Saeedeh Parsaeefard, Mohammad Ali Maddah-Ali, Toktam Mahmoodi, and Babak Hossein Khalaj. "vSPACE: VNF Simultaneous Placement, Admission Control and Embedding." IEEE Journal on Selected Areas in Communications 36, no. 3 (March 2018): 542–57. http://dx.doi.org/10.1109/jsac.2018.2815318.

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9

Nguyen, Tri-Hai, and Myungsik Yoo. "A VNF Placement Optimization Framework for Network Function Virtualization." Journal of Korean Institute of Communications and Information Sciences 44, no. 10 (October 31, 2019): 1956–60. http://dx.doi.org/10.7840/kics.2019.44.10.1956.

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10

Wu, Yunyi, Weichang Zheng, Yongbing Zhang, and Jie Li. "Reliability-Aware VNF Placement Using a Probability-Based Approach." IEEE Transactions on Network and Service Management 18, no. 3 (September 2021): 2478–91. http://dx.doi.org/10.1109/tnsm.2021.3093199.

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11

Qi, Dandan, Subin Shen, and Guanghui Wang. "Towards an efficient VNF placement in network function virtualization." Computer Communications 138 (April 2019): 81–89. http://dx.doi.org/10.1016/j.comcom.2019.03.005.

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12

Zeng, Zhihao, Zixiang Xia, Xiaoning Zhang, and Yexiao He. "SFC Design and VNF Placement Based on Traffic Volume Scaling and VNF Dependency in 5G Networks." Computer Modeling in Engineering & Sciences 134, no. 3 (2023): 1791–814. http://dx.doi.org/10.32604/cmes.2022.021648.

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13

Ruiz, Lidia, Ramón Durán, Ignacio de Miguel, Pouria Khodashenas, Jose-Juan Pedreño-Manresa, Noemí Merayo, Juan Aguado, et al. "A Genetic Algorithm for VNF Provisioning in NFV-Enabled Cloud/MEC RAN Architectures." Applied Sciences 8, no. 12 (December 13, 2018): 2614. http://dx.doi.org/10.3390/app8122614.

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5G technologies promise to bring new network and service capacities and are expected to introduce significant architectural and service deployment transformations. The Cloud-Radio Access Networks (C-RAN) architecture, enabled by the combination of Software Defined Networking (SDN), Network Function Virtualization (NFV) and Mobile Edge Computing (MEC) technologies, play a key role in the development of 5G. In this context, this paper addresses the problems of Virtual Network Functions (VNF) provisioning (VNF-placement and service chain allocation) in a 5G network. In order to solve that problem, we propose a genetic algorithm that, considering both computing resources and optical network capacity, minimizes both the service blocking rate and CPU usage. In addition, we present an algorithm extension that adds a learning stage and evaluate the algorithm performance benefits in those scenarios where VNF allocations can be reconfigured. Results reveal and quantify the advantages of reconfiguring the VNF mapping depending on the current demands. Our methods outperform previous proposals in the literature, reducing the service blocking ratio while saving energy by reducing the number of active core CPUs.

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14

Xu, Yansen, and Ved P. Kafle. "An Availability-Enhanced Service Function Chain Placement Scheme in Network Function Virtualization." Journal of Sensor and Actuator Networks 8, no. 2 (June 14, 2019): 34. http://dx.doi.org/10.3390/jsan8020034.

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A service function chain (SFC) is an ordered virtual network function (VNF) chain for processing traffic flows to deliver end-to-end network services in a virtual networking environment. A challenging problem for an SFC in this context is to determine where to deploy VNFs and how to route traffic between VNFs of an SFC on a substrate network. In this paper, we formulate an SFC placement problem as an integer linear programing (ILP) model, and propose an availability-enhanced VNF placing scheme based on the layered graphs approach. To improve the availability of SFC deployment, our scheme distributes VNFs of an SFC to multiple substrate nodes to avoid a single point of failure. We conduct numerical analysis and computer simulation to validate the feasibility of our SFC scheme. The results show that the proposed scheme outperforms well in different network scenarios in terms of end-to-end delay of the SFC and computation time cost.

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15

Guo, Shuang, Yarong Du, and Liang Liu. "A Meta Reinforcement Learning Approach for SFC Placement in Dynamic IoT-MEC Networks." Applied Sciences 13, no. 17 (September 3, 2023): 9960. http://dx.doi.org/10.3390/app13179960.

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In order to achieve reliability, security, and scalability, the request flow in the Internet of Things (IoT) needs to pass through the service function chain (SFC), which is composed of series-ordered virtual network functions (VNFs), then reach the destination application in multiaccess edge computing (MEC) for processing. Since there are usually multiple identical VNF instances in the network and the network environment of IoT changes dynamically, placing the SFC for the IoT request flow is a significant challenge. This paper decomposes the dynamic SFC placement problem of the IoT-MEC network into two subproblems: VNF placement and path determination of routing. We first formulate these two subproblems as Markov decision processes. We then propose a meta reinforcement learning and fuzzy logic-based dynamic SFC placement approach (MRLF-SFCP). The MRLF-SFCP contains an inner model that focuses on making SFC placement decisions and an outer model that focuses on learning the initial parameters considering the dynamic IoT-MEC environment. Specifically, the approach uses fuzzy logic to pre-evaluate the link status information of the network by jointly considering available bandwidth, delay, and packet loss rate, which is helpful for model training and convergence. In comparison to existing algorithms, simulation results demonstrate that the MRLF-SFCP algorithm exhibits superior performance in terms of traffic acceptance rate, throughput, and the average reward.

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16

Varasteh, Amir, Basavaraj Madiwalar, Amaury Van Bemten, Wolfgang Kellerer, and Carmen Mas-Machuca. "Holu: Power-Aware and Delay-Constrained VNF Placement and Chaining." IEEE Transactions on Network and Service Management 18, no. 2 (June 2021): 1524–39. http://dx.doi.org/10.1109/tnsm.2021.3055693.

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17

Golkarifard, Morteza, Carla Fabiana Chiasserini, Francesco Malandrino, and Ali Movaghar. "Dynamic VNF placement, resource allocation and traffic routing in 5G." Computer Networks 188 (April 2021): 107830. http://dx.doi.org/10.1016/j.comnet.2021.107830.

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18

TABOTA, Kohei, and Takuji TACHIBANA. "Greedy-Based VNF Placement Algorithm for Dynamic Multipath Service Chaining." IEICE Transactions on Communications E102.B, no. 3 (March 1, 2019): 429–38. http://dx.doi.org/10.1587/transcom.2018nvp0006.

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19

Gao, Tao, Xin Li, Yu Wu, Weixia Zou, Shanguo Huang, Massimo Tornatore, and Biswanath Mukherjee. "Cost-Efficient VNF Placement and Scheduling in Public Cloud Networks." IEEE Transactions on Communications 68, no. 8 (August 2020): 4946–59. http://dx.doi.org/10.1109/tcomm.2020.2992504.

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20

Dwiardhika, Dhanu, and Takuji Tachibana. "Virtual Network Embedding Based on Security Level with VNF Placement." Security and Communication Networks 2019 (February 3, 2019): 1–11. http://dx.doi.org/10.1155/2019/5640134.

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In this paper, in order to embed virtual networks by considering network security, we propose a virtual network embedding based on security level with VNF placement. In this method, virtual networks are embedded in a substrate network by considering security and some security VNFs are placed in order to increase the security level of substrate networks. By using our proposed method, many virtual networks can be embedded by considering security level. As a result, the reward can be increased and the cost of placing VNFs is not increased so much. We evaluate the performance of our proposed method with simulation. The performance of this method is compared with the performance of a method that places VNFs randomly and the performance of a method without placing VNFs. From numerical examples, we investigate the effectiveness of this method. In numerical examples, we show that the proposed method is effective in embedding virtual networks by considering network security.

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21

Khoshkholghi, Mohammad Ali, Michel Gokan Khan, Kyoomars Alizadeh Noghani, Javid Taheri, Deval Bhamare, Andreas Kassler, Zhengzhe Xiang, Shuiguang Deng, and Xiaoxian Yang. "Service Function Chain Placement for Joint Cost and Latency Optimization." Mobile Networks and Applications 25, no. 6 (November 21, 2020): 2191–205. http://dx.doi.org/10.1007/s11036-020-01661-w.

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AbstractNetwork Function Virtualization (NFV) is an emerging technology to consolidate network functions onto high volume storages, servers and switches located anywhere in the network. Virtual Network Functions (VNFs) are chained together to provide a specific network service, called Service Function Chains (SFCs). Regarding to Quality of Service (QoS) requirements and network features and states, SFCs are served through performing two tasks: VNF placement and link embedding on the substrate networks. Reducing deployment cost is a desired objective for all service providers in cloud/edge environments to increase their profit form demanded services. However, increasing resource utilization in order to decrease deployment cost may lead to increase the service latency and consequently increase SLA violation and decrease user satisfaction. To this end, we formulate a multi-objective optimization model to joint VNF placement and link embedding in order to reduce deployment cost and service latency with respect to a variety of constraints. We, then solve the optimization problem using two heuristic-based algorithms that perform close to optimum for large scale cloud/edge environments. Since the optimization model involves conflicting objectives, we also investigate pareto optimal solution so that it optimizes multiple objectives as much as possible. The efficiency of proposed algorithms is evaluated using both simulation and emulation. The evaluation results show that the proposed optimization approach succeed in minimizing both cost and latency while the results are as accurate as optimal solution obtained by Gurobi (5%).

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22

Engelmann, Anna, and Admela Jukan. "A Combinatorial Reliability Analysis of Generic Service Function Chains in Data Center Networks." ACM Transactions on Modeling and Performance Evaluation of Computing Systems 6, no. 3 (September 30, 2021): 1–24. http://dx.doi.org/10.1145/3477046.

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In data center networks, the reliability of Service Function Chain (SFC)—an end-to-end service presented by a chain of virtual network functions (VNFs)—is a complex and specific function of placement, configuration, and application requirements, both in hardware and software. Existing approaches to reliability analysis do not jointly consider multiple features of system components, including, (i) heterogeneity, (ii) disjointness, (iii) sharing, (iv) redundancy, and (v) failure interdependency. To this end, we develop a novel analysis of service reliability of the so-called generic SFC, consisting of n = k + r sub-SFCs, whereby k ≥ 1 and r ≥ 0 are the numbers of arbitrary placed primary and backup (redundant) sub-SFCs, respectively. Our analysis is based on combinatorics and a reduced binomial theorem—resulting in a simple approach, which, however, can be utilized to analyze rather complex SFC configurations. The analysis is practically applicable to various VNF placement strategies in arbitrary data center configurations, and topologies and can be effectively used for evaluation and optimization of reliable SFC placements.

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23

Sallam, Gamal, and Bo Ji. "Joint Placement and Allocation of VNF Nodes With Budget and Capacity Constraints." IEEE/ACM Transactions on Networking 29, no. 3 (June 2021): 1238–51. http://dx.doi.org/10.1109/tnet.2021.3058378.

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24

Sun, Penghao, Julong Lan, Junfei Li, Zehua Guo, and Yuxiang Hu. "Combining Deep Reinforcement Learning With Graph Neural Networks for Optimal VNF Placement." IEEE Communications Letters 25, no. 1 (January 2021): 176–80. http://dx.doi.org/10.1109/lcomm.2020.3025298.

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25

Pei, Jianing, Peilin Hong, Miao Pan, Jiangqing Liu, and Jingsong Zhou. "Optimal VNF Placement via Deep Reinforcement Learning in SDN/NFV-Enabled Networks." IEEE Journal on Selected Areas in Communications 38, no. 2 (February 2020): 263–78. http://dx.doi.org/10.1109/jsac.2019.2959181.

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26

Bunyakitanon, Monchai, Xenofon Vasilakos, Reza Nejabati, and Dimitra Simeonidou. "End-to-End Performance-Based Autonomous VNF Placement With Adopted Reinforcement Learning." IEEE Transactions on Cognitive Communications and Networking 6, no. 2 (June 2020): 534–47. http://dx.doi.org/10.1109/tccn.2020.2988486.

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27

Araújo, Samuel M. A., Fernanda S. H. de Souza, and Geraldo R. Mateus. "A hybrid optimization-Machine Learning approach for the VNF placement and chaining problem." Computer Networks 199 (November 2021): 108474. http://dx.doi.org/10.1016/j.comnet.2021.108474.

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28

Abu-Lebdeh, Mohammad, Diala Naboulsi, Roch Glitho, and Constant Wette Tchouati. "On the Placement of VNF Managers in Large-Scale and Distributed NFV Systems." IEEE Transactions on Network and Service Management 14, no. 4 (December 2017): 875–89. http://dx.doi.org/10.1109/tnsm.2017.2730199.

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29

Xuan, Hejun, Shiwei Wei, Yan Feng, Daohua Liu, and Yanling Li. "Bi-Level Programming Model and Algorithm for VNF Deployment With Data Centers Placement." IEEE Access 7 (2019): 185760–72. http://dx.doi.org/10.1109/access.2019.2960395.

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30

Marchetto, Guido, Riccardo Sisto, Fulvio Valenza, Jalolliddin Yusupov, and Adlen Ksentini. "A Formal Approach to Verify Connectivity and Optimize VNF Placement in Industrial Networks." IEEE Transactions on Industrial Informatics 17, no. 2 (February 2021): 1515–25. http://dx.doi.org/10.1109/tii.2020.3002816.

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31

Cappanera, Paola, Federica Paganelli, and Francesca Paradiso. "VNF placement for service chaining in a distributed cloud environment with multiple stakeholders." Computer Communications 133 (January 2019): 24–40. http://dx.doi.org/10.1016/j.comcom.2018.10.008.

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32

Mahboob, Tahira, Young Rok Jung, and Min Young Chung. "Dynamic VNF Placement to Manage User Traffic Flow in Software-Defined Wireless Networks." Journal of Network and Systems Management 28, no. 3 (March 13, 2020): 436–56. http://dx.doi.org/10.1007/s10922-020-09520-5.

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33

Ros, Seyha, Prohim Tam, Inseok Song, Seungwoo Kang, and Seokhoon Kim. "Handling Efficient VNF Placement with Graph-Based Reinforcement Learning for SFC Fault Tolerance." Electronics 13, no. 13 (June 28, 2024): 2552. http://dx.doi.org/10.3390/electronics13132552.

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Network functions virtualization (NFV) has become the platform for decomposing the sequence of virtual network functions (VNFs), which can be grouped as a forwarding graph of service function chaining (SFC) to serve multi-service slice requirements. NFV-enabled SFC consists of several challenges in reaching the reliability and efficiency of key performance indicators (KPIs) in management and orchestration (MANO) decision-making control. The problem of SFC fault tolerance is one of the most critical challenges for provisioning service requests, and it needs resource availability. In this article, we proposed graph neural network (GNN)-based deep reinforcement learning (DRL) to enhance SFC fault tolerance (GRL-SFT), which targets the chain graph representation, long-term approximation, and self-organizing service orchestration for future massive Internet of Everything applications. We formulate the problem as the Markov decision process (MDP). DRL seeks to maximize the cumulative rewards by maximizing the service request acceptance ratios and minimizing the average completion delays. The proposed model solves the VNF management problem in a short time and configures the node allocation reliably for real-time restoration. Our simulation result demonstrates the effectiveness of the proposed scheme and indicates better performance in terms of total rewards, delays, acceptances, failures, and restoration ratios in different network topologies compared to reference schemes.

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34

Manzanares-Lopez, Pilar, Juan Pedro Muñoz-Gea, and Josemaria Malgosa-Sanahuja. "VNF Placement for Service Function Chains with Strong Low-Delay Restrictions in Edge Computing Networks." Applied Sciences 10, no. 18 (September 20, 2020): 6573. http://dx.doi.org/10.3390/app10186573.

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The edge computing paradigm, allowing the location of network services close to end users, defines new network scenarios. One of them considers the existence of micro data centers, with reduced resources but located closer to service requesters, to complement remote cloud data centers. This hierarchical and geo-distributed architecture allows the definition of different time constraints that can be taken into account when mapping services into data centers. This feature is especially useful in the Virtual Network Function (VNF) placement problem, where the network functions composing a Service Function Chain (SFC) may require more or less strong delay restrictions. We propose the ModPG (Modified Priority-based Greedy) heuristic, a VNF placement solution that weighs the latency, bandwidth, and resource restrictions, but also the instantiation cost of VNFs. ModPG is an improved solution of a previous proposal (called PG). Although both heuristics share the same optimization target, that is the reduction of the total substrate resource cost, the ModPG heuristic identifies and solves a limitation of the PG solution: the mapping of sets of SFCs that include a significant proportion of SFC requests with strong low-delay restrictions. Unlike PG heuristic performance evaluation, where the amount of SFC requests with strong low-delay restrictions is not considered as a factor to be analyzed, in this work, both solutions are compared considering the presence of 1%, 15%, and 25% of this type of SFC request. Results show that the ModPG heuristic optimizes the target cost similarly to the original proposal, and at the same time, it offers a better performance when a significant number of low-delay demanding SFC requests are present.

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35

Agarwal, Satyam, Francesco Malandrino, Carla Fabiana Chiasserini, and Swades De. "VNF Placement and Resource Allocation for the Support of Vertical Services in 5G Networks." IEEE/ACM Transactions on Networking 27, no. 1 (February 2019): 433–46. http://dx.doi.org/10.1109/tnet.2018.2890631.

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36

Bunyakitanon, Monchai, Aloizio Pereira da Silva, Xenofon Vasilakos, Reza Nejabati, and Dimitra Simeonidou. "Auto-3P: An autonomous VNF performance prediction & placement framework based on machine learning." Computer Networks 181 (November 2020): 107433. http://dx.doi.org/10.1016/j.comnet.2020.107433.

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37

Tajiki, Mohammad M., Stefano Salsano, Luca Chiaraviglio, Mohammad Shojafar, and Behzad Akbari. "Joint Energy Efficient and QoS-Aware Path Allocation and VNF Placement for Service Function Chaining." IEEE Transactions on Network and Service Management 16, no. 1 (March 2019): 374–88. http://dx.doi.org/10.1109/tnsm.2018.2873225.

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38

Pham, Chuan, Nguyen H. Tran, Shaolei Ren, Walid Saad, and Choong Seon Hong. "Traffic-Aware and Energy-Efficient vNF Placement for Service Chaining: Joint Sampling and Matching Approach." IEEE Transactions on Services Computing 13, no. 1 (January 1, 2020): 172–85. http://dx.doi.org/10.1109/tsc.2017.2671867.

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39

Dang-Quang, Nhat-Minh, and Myungsik Yoo. "Optimized placement of symmetrical service function chain in network function virtualization." Computer Science and Information Systems, no. 00 (2022): 6. http://dx.doi.org/10.2298/csis210920006d.

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Network function virtualization (NFV) is one of the key technology enablers for actualizing 5G networks. With NFV, virtual network functions (VNFs) are linked together as a service function chain (SFC), which provides network functionality for the customer on demand. However, how to efficiently find a suitable placement for VNFs regarding the given objectives is an extremely difficult issue. The existing approaches assume that the SFC has a simple and asymmetrical pattern that is unsuitable to modeling a real system. We address this limitation by studying a VNF placement optimization problem with symmetrical SFCs that can support both symmetric and asymmetric traffic flows. This NP-hard problem is formulated as a mixed-integer linear programming (MILP) model. An iterative greedy-based heuristic is proposed to overcome the complexity of the MILP model. Extensive simulation results show that the proposed heuristic can obtain a near-optimal solution compared to MILP for a small-scale network, and at the same time, is superior to a traditional heuristic for a large-scale network.

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Pham, Tuan-Minh, and Thi-Minh Nguyen. "Optimizing Traffic Engineering for Resilient Services in NFV-Based Connected Autonomous Vehicles." Sensors 21, no. 24 (December 17, 2021): 8446. http://dx.doi.org/10.3390/s21248446.

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The massive amount of data generated daily by various sensors equipped with connected autonomous vehicles (CAVs) can lead to a significant performance issue of data processing and transfer. Network Function Virtualization (NFV) is a promising approach to improving the performance of a CAV system. In an NFV framework, Virtual Network Function (VNF) instances can be placed in edge and cloud servers and connected together to enable a flexible CAV service with low latency. However, protecting a service function chain composed of several VNFs from a failure is challenging in an NFV-based CAV system (VCAV). We propose an integer linear programming (ILP) model and two approximation algorithms for resilient services to minimize the service disruption cost in a VCAV system when a failure occurs. The ILP model, referred to as TERO, allows us to obtain the optimal solution for traffic engineering, including the VNF placement and routing for resilient services with regard to dynamic routing. Our proposed algorithms based on heuristics (i.e., TERH) and reinforcement learning (i.e., TERA) provide an approximation solution for resilient services in a large-scale VCAV system. Evaluation results with real datasets and generated network topologies show that TERH and TERA can provide a solution close to the optimal result. It also suggests that TERA should be used in a highly dynamic VCAV system.

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Dieye, Mouhamad, Shohreh Ahvar, Jagruti Sahoo, Ehsan Ahvar, Roch Glitho, Halima Elbiaze, and Noel Crespi. "CPVNF: Cost-Efficient Proactive VNF Placement and Chaining for Value-Added Services in Content Delivery Networks." IEEE Transactions on Network and Service Management 15, no. 2 (June 2018): 774–86. http://dx.doi.org/10.1109/tnsm.2018.2815986.

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Xu, Zichuan, Zhiheng Zhang, Weifa Liang, Qiufen Xia, Omer Rana, and Guowei Wu. "QoS-Aware VNF Placement and Service Chaining for IoT Applications in Multi-Tier Mobile Edge Networks." ACM Transactions on Sensor Networks 16, no. 3 (August 14, 2020): 1–27. http://dx.doi.org/10.1145/3387705.

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Tang, Hong, Danny Zhou, and Duan Chen. "Dynamic Network Function Instance Scaling Based on Traffic Forecasting and VNF Placement in Operator Data Centers." IEEE Transactions on Parallel and Distributed Systems 30, no. 3 (March 1, 2019): 530–43. http://dx.doi.org/10.1109/tpds.2018.2867587.

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Kim, Sanghyeok, Sungyoung Park, Youngjae Kim, Siri Kim, and Kwonyong Lee. "VNF-EQ: dynamic placement of virtual network functions for energy efficiency and QoS guarantee in NFV." Cluster Computing 20, no. 3 (July 1, 2017): 2107–17. http://dx.doi.org/10.1007/s10586-017-1004-3.

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Pandey, Abhishek Kumar, and Sarvpal Singh. "Service Chain Placement by Using an African Vulture Optimization Algorithm Based VNF in Cloud-Edge Computing." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 12 (December 29, 2023): e31509. http://dx.doi.org/10.14201/adcaij.31509.

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The use of virtual network functions (VNFs) enables the implementation of service function chains (SFCs), which is an innovative approach for delivering network services. The deployment of service chains on the actual network infrastructure and the establishment of virtual connections between VNF instances are crucial factors that significantly impact the quality of network services provided. Current research on the allocation of vital VNFs and resource constraints on the edge network has overlooked the potential benefits of employing SFCs with instance reuse. This strategy offers significant improvements in resource utilization and reduced startup time. The proposed approach demonstrates superior performance compared to existing state-of-the-art methods in maintaining inbound service chain requests, even in complex network typologies observed in real-world scenarios. We propose a novel technique called African vulture optimization algorithm for virtual network functions (AVOAVNF), which optimizes the sequential arrangement of SFCs. Extensive simulations on edge networks evaluate the AVOAVNF methodology, considering metrics such as latency, energy consumption, throughput, resource cost, and execution time. The results indicate that the proposed method outperforms BGWO, DDRL, BIP, and MILP techniques, reducing energy consumption by 8.35%, 12.23%, 29.54%, and 52.29%, respectively.

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Arulappan, Arunkumar, Gunasekaran Raja, Kalpdrum Passi, and Aniket Mahanti. "Optimization of 5G/6G Telecommunication Infrastructure through an NFV-Based Element Management System." Symmetry 14, no. 5 (May 10, 2022): 978. http://dx.doi.org/10.3390/sym14050978.

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Network Function Virtualization (NFV) is an enabling technology that brings together automated network service management and corresponding virtualized network functions that use an NFV Infrastructure (NFVI) framework. The Virtual Network Function Manager (VNFM) placement in a large-scale distributed NFV deployment is therefore a challenging task due to the potential negative impact on performance and operating expense cost. The VNFM assigns Virtual Network Functions (VNFs) and operates efficiently based on network demands with resilient performance through efficient placement techniques. The degradation in performance and a tremendous increase in capital expenditure and operating expenses indicated this chaotic problem. This article proposed a method for VNFM placement using information on the resources of each nodes’ Element Manager (EM), which is an efficient method to assign VNFs to each node of element management systems. In addition, this paper proposed an Optimized Element Manager (OEM) method for looking at appropriate EMs for the placement of VNF through periodic information on available resources. It also overcomes challenges such as delays and variations in VNFs workload for edge computing and distributed cloud regions. The performance is measured based on computations performed on various optimization algorithms such as linear programming and tabu search algorithms. The advent of the new service provisioning model of BGP-EVPN for VXLAN is materialized by integrating VTS with OpenStack. The numerical analysis shows that the proposed OEM algorithm gives an optimal solution with an average gap of 8%.

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Baldi, Mario, and Amedeo Sapio. "Network Function Modeling and Performance Estimation." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (October 1, 2018): 3021. http://dx.doi.org/10.11591/ijece.v8i5.pp3021-3037.

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<p>This work introduces a methodology for the modelization of network functions focused on the identification of recurring execution patterns as basic building blocks and aimed at providing a platform independent representation. By mapping each modeling building block on specific hardware, the performance of the network function can be estimated in termsof maximum throughput that the network function can achieve on the specific execution platform. The approach is such that once the basic modeling building blocks have been mapped, the estimate can be computed automatically for any modeled network function. Experimental results on several sample network functions show that although our approach cannot be very accurate without taking in consideration traffic characteristics, it is very valuable for those application where even loose estimates are key. One such example is orchestration in network functions virtualization (NFV) platforms, as well as in general virtualization platforms where virtual machine placement is based also on the performance<br />of network services offered to them. Being able to automatically estimate the performance of a virtualized network function (VNF) on different execution hardware, enables optimal placement of VNFs themselves as well as the virtual hosts they serve, while efficiently utilizing available resources.</p>

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Jahedi, Zahra, and Thomas Kunz. "The Value of Simple Heuristics for Virtualized Network Function Placement." Future Internet 12, no. 10 (September 25, 2020): 161. http://dx.doi.org/10.3390/fi12100161.

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Network Function Virtualization (NFV) can lower the CAPEX and/or OPEX for service providers and allow for quick deployment of services. Along with the advantages come some challenges. The main challenge in the use of Virtualized Network Functions (VNF) is the VNFs’ placement in the network. There is a wide range of mathematical models proposed to place the Network Functions (NF) optimally. However, the critical problem of mathematical models is that they are NP-hard, and consequently not applicable to larger networks. In wireless networks, we are considering the scarcity of Bandwidth (BW) as another constraint that is due to the presence of interference. While there exist many efforts in designing a heuristic model that can provide solutions in a timely manner, the primary focus with such heuristics was almost always whether they provide results almost as good as optimal solution. Consequently, the heuristics themselves become quite non-trivial, and solving the placement problem for larger networks still takes a significant amount of time. In this paper, in contrast, we focus on designing a simple and scalable heuristic. We propose four heuristics, which are gradually becoming more complex. We compare their performance with each other, a related heuristic proposed in the literature, and a mathematical optimization model. Our results demonstrate that while more complex placement heuristics do not improve the performance of the algorithm in terms of the number of accepted placement requests, they take longer to solve and therefore are not applicable to larger networks.In contrast, a very simple heuristic can find near-optimal solutions much faster than the other more complicated heuristics while keeping the number of accepted requests close to the results achieved with an NP-hard optimization model.

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Li, Wenjing, Yueqi Zi, Lei Feng, Fanqing Zhou, Peng Yu, and Xuesong Qiu. "Latency-Optimal Virtual Network Functions Resource Allocation for 5G Backhaul Transport Network Slicing." Applied Sciences 9, no. 4 (February 18, 2019): 701. http://dx.doi.org/10.3390/app9040701.

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The concept of network slicing (NS) has been proposed for flexible resource provisioning where a physical resource is partitioned into logically independent networks on demand. The NS resource allocation implies the definition of a feasible path in the infrastructure network with adequate resource availability. However, due to complex structural characteristics of the backhaul transport network, a number of issues arise when fast deploying the end-to-end (E2E) slices onto network infrastructures. In this paper, a pair-decision resource allocation model is firstly formulated to construct the mapping relationship between logical networks and substrate networks in a coordinated way. In order to improve extreme quality of service (QoS) and user experiment, latency-optimal virtual resource allocation problem is defined, subject to the backhaul capacity and bandwidth constraints. The problem is formulated as an integer linear programming (ILP) and solved with the branch-and-bound scheme, whose resolution yields an optimal virtual network function (VNF) placement and traffic routing policy. Numerical results reveal that the proposed scheme can enable the transport network latency optimization with a reduction of up to 30% and 41.6% compared to the Network Slice Design Problem (NSDP) and Random Fit Placement Algorithm (RFPA) schemes respectively. In the meanwhile, the network load balance and serviceability have been improved efficiently with better resource utilization as well.

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Majeed, Abdul, Abdullah M. Alnajim, Athar Waseem, Aleem Khaliq, Aqdas Naveed, Shabana Habib, Muhammad Islam, and Sheroz Khan. "Deep Learning-Based Symptomizing Cyber Threats Using Adaptive 5G Shared Slice Security Approaches." Future Internet 15, no. 6 (May 26, 2023): 193. http://dx.doi.org/10.3390/fi15060193.

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In fifth Generation (5G) networks, protection from internal attacks, external breaches, violation of confidentiality, and misuse of network vulnerabilities is a challenging task. Various approaches, especially deep-learning (DL) prototypes, have been adopted in order to counter such challenges. For 5G network defense, DL module are recommended here in order to symptomize suspicious NetFlow data. This module behaves as a virtual network function (VNF) and is placed along a 5G network. The DL module as a cyber threat-symptomizing (CTS) unit acts as a virtual security scanner along the 5G network data analytic function (NWDAF) to monitor the network data. When the data were found to be suspicious, causing network bottlenecks and let-downs of end-user services, they were labeled as “Anomalous”. For the best proactive and adaptive cyber defense system (PACDS), a logically organized modular approach has been followed to design the DL security module. In the application context, improvements have been made to input features dimension and computational complexity reduction with better response times and accuracy in outlier detection. Moreover, key performance indicators (KPIs) have been proposed for security module placement to secure interslice and intraslice communication channels from any internal or external attacks, also suggesting an adaptive defense mechanism and indicating its placement on a 5G network. Among the chosen DL models, the CNN model behaves as a stable model during behavior analysis in the results. The model classifies botnet-labeled data with 99.74% accuracy and higher precision.

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