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Gotowa bibliografia na temat „Placement des VNF”

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Artykuły w czasopismach na temat "Placement des VNF"

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

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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 i Ana Cristina Bernardo de Oliveira. "A Novel Strategy for VNF Placement in Edge Computing Environments". Future Internet 14, nr 12 (30.11.2022): 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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Wu, Xing, Jing Duan, Mingyu Zhong, Peng Li i Jianjia Wang. "VNF Chain Placement for Large Scale IoT of Intelligent Transportation". Sensors 20, nr 14 (8.07.2020): 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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Umrao, Brajesh Kumar, i Dharmendar Kumar Yadav. "APVNFC: Adaptive Placement of Virtual Network Function Chains". Cybernetics and Information Technologies 23, nr 1 (1.03.2023): 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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Abdelaal, Marwa A., Gamal A. Ebrahim i Wagdy R. Anis. "Efficient Placement of Service Function Chains in Cloud Computing Environments". Electronics 10, nr 3 (30.01.2021): 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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Tao, Xiaoyi, Kaoru Ota, Mianxiong Dong, Heng Qi i Keqiu Li. "Cost as Performance: VNF Placement at the Edge". IEEE Networking Letters 3, nr 2 (czerwiec 2021): 70–74. http://dx.doi.org/10.1109/lnet.2021.3065651.

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Leivadeas, Aris, George Kesidis, Mohamed Ibnkahla i Ioannis Lambadaris. "VNF Placement Optimization at the Edge and Cloud †". Future Internet 11, nr 3 (9.03.2019): 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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Tahmasbi Nejad, Mohammad Ali, Saeedeh Parsaeefard, Mohammad Ali Maddah-Ali, Toktam Mahmoodi i Babak Hossein Khalaj. "vSPACE: VNF Simultaneous Placement, Admission Control and Embedding". IEEE Journal on Selected Areas in Communications 36, nr 3 (marzec 2018): 542–57. http://dx.doi.org/10.1109/jsac.2018.2815318.

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Nguyen, Tri-Hai, i Myungsik Yoo. "A VNF Placement Optimization Framework for Network Function Virtualization". Journal of Korean Institute of Communications and Information Sciences 44, nr 10 (31.10.2019): 1956–60. http://dx.doi.org/10.7840/kics.2019.44.10.1956.

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Wu, Yunyi, Weichang Zheng, Yongbing Zhang i Jie Li. "Reliability-Aware VNF Placement Using a Probability-Based Approach". IEEE Transactions on Network and Service Management 18, nr 3 (wrzesień 2021): 2478–91. http://dx.doi.org/10.1109/tnsm.2021.3093199.

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Rozprawy doktorskie na temat "Placement des VNF"

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Ochoa, Aday Leonardo. "Contributions to topology discovery, self-healing and VNF placement in software-defined and virtualized networks". Doctoral thesis, Universitat Politècnica de Catalunya, 2019. http://hdl.handle.net/10803/665397.

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The evolution of information and communication technologies (e.g. cloud computing, the Internet of Things (IoT) and 5G, among others) has enabled a large market of applications and network services for a massive number of users connected to the Internet. Achieving high programmability while decreasing complexity and costs has become an essential aim of networking research due to the ever-increasing pressure generated by these applications and services. However, meeting these goals is an almost impossible task using traditional IP networks. Software-Defined Networking (SDN) is an emerging network architecture that could address the needs of service providers and network operators. This new technology consists in decoupling the control plane from the data plane, enabling the centralization of control functions on a concentrated or distributed platform. It also creates an abstraction between the network infrastructure and network applications, which allows for designing more flexible and programmable networks. Recent trends of increased user demands, the explosion of Internet traffic and diverse service requirements have further driven the interest in the potential capabilities of SDN to enable the introduction of new protocols and traffic management models. This doctoral research is focused on improving high-level policies and control strategies, which are becoming increasingly important given the limitations of current solutions for large-scale SDN environments. Specifically, the three largest challenges addressed in the development of this thesis are related to the processes of topology discovery, fault recovery and Virtual Network Function (VNF) placement in software-defined and virtualized networks. These challenges led to the design of a set of effective techniques, ranging from network protocols to optimal and heuristic algorithms, intended to solve existing problems and contribute to the deployment and adoption of such programmable networks.For the first challenge, this work presents a novel protocol that, unlike existing approaches, enables a distributed layer 2 discovery without the need for previous IP configurations or controller knowledge of the network. By using this mechanism, the SDN controller can discover the network view without incurring scalability issues, while taking advantage of the shortest control paths toward each switch. Moreover, this novel approach achieves noticeable improvement with respect to state-of-the-art techniques. To address the resilience concern of SDN, we propose a self-healing mechanism that recovers the control plane connectivity in SDN-managed environments without overburdening the controller performance. The main idea underlying this proposal is to enable real-time recovery of control paths in the face of failures without the intervention of a controller. Obtained results show that the proposed approach recovers the control topology efficiently in terms of time and message load over a wide range of generated networks. The third contribution made in this thesis combines topology knowledge with bin packing techniques in order to efficiently place the required VNF. An online heuristic algorithm with low-complexity was developed as a suitable solution for dynamic infrastructures. Extensive simulations, using network topologies representative of different scales, validate the good performance of the proposed approaches regarding the number of required instances and the delay among deployed functions. Additionally, the proposed heuristic algorithm improves the execution times by a fifth order of magnitude compared to the optimal formulation of this problem.
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Ahvar, Shohreh. "Resource allocation in cloud and Content Delivery Network (CDN)". Thesis, Evry, Institut national des télécommunications, 2018. http://www.theses.fr/2018TELE0011.

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L’objectif de cette thèse est de présenter de nouveaux algorithmes de répartition des ressources sous la forme de machines virtuelles (VMs) et fonction de réseau virtuel (VNFs) dans les Clouds et réseaux de diffusion de contenu (CDNs). La thèse comprend deux principales parties: la première se concentre sur la rentabilité des Clouds distribués, et développe ensuite les raisons d’optimiser les coûts ainsi que les émissions de carbone. Cette partie comprend quatre contributions. La première contribution est une étude de l’état de l’art sur la répartition des coûts et des émissions de carbone dans les environnements de clouds distribués. La deuxième contribution propose une méthode d’allocation des ressources, appelée NACER, pour les clouds distribués. La troisième contribution présente une méthode de placement VM efficace en termes de coûts et de carbone (appelée CACEV) pour les clouds distribués verts. Pour obtenir une meilleure performance, la quatrième contribution propose une méthode dynamique de placement VM (D-CACEV) pour les clouds distribués. La deuxième partie propose des algorithmes de placement de VNFs dans les Clouds et réseaux de CDNs pour optimiser les coûts. Cette partie comprend cinq contributions. Une étude de l’état de l’art sur les solutions proposées est le but de la première contribition. La deuxième contribution propose une méthode d’allocation des ressources, appelée CCVP, pour le provisionnement de service réseau dans les clouds et réseaux de ISP. La troisième contribution implémente le résultat de l’algorithme CCVP dans une plateforme réelle. La quatrième contribution considère l’effet de la permutation de VNFs dans les chaîne de services et la cinquième contribution explique le placement de VNFs pour les services à valeur ajoutée dans les CDNs
High energy costs and carbon emissions are two significant problems in distributed computing domain, such as distributed clouds and Content Delivery Networks (CDNs). Resource allocation methods (e.g., in form of Virtual Machine (VM) or Virtual Network Function (VNF) placement algorithms) have a direct effect on cost, carbon emission and Quality of Service (QoS). This thesis includes three related parts. First, it targets the problem of resource allocation (i.e., in the form of network aware VM placement algorithms) for distributed clouds and proposes cost and carbon emission efficient resource allocation algorithms for green distributed clouds. Due to the similarity of the network-aware VM placement problem in distributed clouds with a VNF placement problem, the second part of the thesis, getting experience from the first part, proposes a new cost efficient resource allocation algorithm (i.e., VNF placement) for network service provision in data centers and Internet Service Provider (ISP) network. Finally, the last part of the thesis presents new cost efficient resource allocation algorithms (i.e., VNF placement) for value-added service provisioning in NFV-based CDNs
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Yala, Louiza. "Content Delivery Networks as a Service (CDNaaS)". Thesis, Rennes 1, 2018. http://www.theses.fr/2018REN1S097/document.

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Le but de cette thèse est d’étudier et d’évaluer le rôle de la virtualisation des réseau de diffusion de contenu. Nous proposons une implémentation d’une architecture CDN permettant à un opérateur de réseau de virtualiser son infrastructure CDN et de la louer à des fournisseurs de contenu. Afin d’avoir une allocation optimale des ressources, nous proposons une méthode qui combine les informations fournies lors de la demande par le fournisseur de contenu avec les données du réseau et de l’infrastructure de calcul. Nous avons modélisé ce problème d’allocation de ressources en problème d’optimisation, résolu par un algorithme. Les résultats obtenues donnent suite à la proposition d’algorithmes et d’heuristiques de placement pour l’allocation conjointe de vCPU-à-VM et le placement des VMs dans les Pms
The goal of this thesis is to study and evaluate the role a Virtual CDNs in improving the end-users QoE while saving on service providers’ costs and service availability. First, we present the design and implementation of an architecture for on-demand deployment of a vCDN infrastructure over a telco cloud. Second, we propose different algorithms for solving the Virtual Network Function (VNF) placement problem. We propose a polynomialtime heuristic algorithms to solve a relaxed version of the problem’s assumptions, we show experimentally that the derived solutions are close to the optimal. Finally, we study and evaluate solutions for the placement of VNF at the edge, by moving from the traditional central cloud to the edge one. We have also shown how our method can reduce delays and still provide a highly-available service
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Subramanya, Tejas. "Autonomic Management and Orchestration Strategies in MEC-Enabled 5G Networks". Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/320883.

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5G and beyond mobile network technology promises to deliver unprecedented ultra-low latency and high data rates, paving the way for many novel applications and services. Network Function Virtualization (NFV) and Multi-access Edge Computing (MEC) are two technologies expected to play a vital role in achieving ambitious Quality of Service requirements of such applications. While NFV provides flexibility by enabling network functions to be dynamically deployed and inter-connected to realize Service Function Chains (SFC), MEC brings the computing capability to the mobile network's edges, thus reducing latency and alleviating the transport network load. However, adequate mechanisms are needed to meet the dynamically changing network service demands (i.e., in single and multiple domains) and optimally utilize the network resources while ensuring that the end-to-end latency requirement of services is always satisfied. In this dissertation work, we break the problem into three separate stages and present the solutions for each one of them.Firstly, we apply Artificial Intelligence (AI) techniques to drive NFV resource orchestration in MEC-enabled 5G architectures for single and multi-domain scenarios. We propose three deep learning approaches to perform horizontal and vertical Virtual Network Function (VNF) auto-scaling: (i) Multilayer Perceptron (MLP) classification and regression (single-domain), (ii) Centralized Artificial Neural Network (ANN), centralized Long-Short Term Memory (LSTM) and centralized Convolutional Neural Network-LSTM (CNN-LSTM) (single-domain), and (iii) Federated ANN, federated LSTM and federated CNN-LSTM (multi-domain). We evaluate the performance of each of these deep learning models trained over a commercial network operator dataset and investigate the pros and cons of different approaches for VNF auto-scaling. For the first approach, our results show that both MLP classifier and MLP regressor models have strong predicting capability for auto-scaling. However, MLP regressor outperforms MLP classifier in terms of accuracy. For the second approach (one-step prediction), CNN-LSTM performs the best for the QoS-prioritized objective and LSTM performs the best for the cost-prioritized objective. For the second approach (multi-step prediction), the encoder-decoder CNN-LSTM model outperforms the encoder-decoder LSTM model for both QoS and Cost prioritized objectives. For the third approach, both federated LSTM and federated CNN-LSTM models perform equally better than the federated ANN model. It was also noted that in general federated learning approaches performs poorly compared to centralized learning approaches. Secondly, we employ Integer Linear Programming (ILP) techniques to formulate and solve a joint user association and SFC placement problem, where each SFC represents a service requested by a user with end-to-end latency and data rate requirements. We also develop a comprehensive end-to-end latency model considering radio delay, backhaul network delay and SFC processing delay for 5G mobile networks. We evaluated the proposed model using simulations based on real-operator network topology and real-world latency values. Our results show that the average end-to-end latency reduces significantly when SFCs are placed at the ME hosts according to their latency and data rate demands. Furthermore, we propose an heuristic algorithm to address the issue of scalability in ILP, that can solve the above association/mapping problem in seconds rather than hours.Finally, we introduce lightMEC - a lightweight MEC platform for deploying mobile edge computing functionalities which allows hosting of low-latency and bandwidth-intensive applications at the network edge. Measurements conducted over a real-life test demonstrated that lightMEC could actually support practical MEC applications without requiring any change to existing mobile network nodes' functionality in the access and core network segments. The significant benefits of adopting the proposed architecture are analyzed based on a proof-of-concept demonstration of the content caching use case. Furthermore, we introduce the AI-driven Kubernetes orchestration prototype that we implemented by leveraging the lightMEC platform and assess the performance of the proposed deep learning models (from stage 1) in an experimental setup. The prototype evaluations confirm the simulation results achieved in stage 1 of the thesis.
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Ahvar, Shohreh. "Resource allocation in cloud and Content Delivery Network (CDN)". Electronic Thesis or Diss., Evry, Institut national des télécommunications, 2018. http://www.theses.fr/2018TELE0011.

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L’objectif de cette thèse est de présenter de nouveaux algorithmes de répartition des ressources sous la forme de machines virtuelles (VMs) et fonction de réseau virtuel (VNFs) dans les Clouds et réseaux de diffusion de contenu (CDNs). La thèse comprend deux principales parties: la première se concentre sur la rentabilité des Clouds distribués, et développe ensuite les raisons d’optimiser les coûts ainsi que les émissions de carbone. Cette partie comprend quatre contributions. La première contribution est une étude de l’état de l’art sur la répartition des coûts et des émissions de carbone dans les environnements de clouds distribués. La deuxième contribution propose une méthode d’allocation des ressources, appelée NACER, pour les clouds distribués. La troisième contribution présente une méthode de placement VM efficace en termes de coûts et de carbone (appelée CACEV) pour les clouds distribués verts. Pour obtenir une meilleure performance, la quatrième contribution propose une méthode dynamique de placement VM (D-CACEV) pour les clouds distribués. La deuxième partie propose des algorithmes de placement de VNFs dans les Clouds et réseaux de CDNs pour optimiser les coûts. Cette partie comprend cinq contributions. Une étude de l’état de l’art sur les solutions proposées est le but de la première contribition. La deuxième contribution propose une méthode d’allocation des ressources, appelée CCVP, pour le provisionnement de service réseau dans les clouds et réseaux de ISP. La troisième contribution implémente le résultat de l’algorithme CCVP dans une plateforme réelle. La quatrième contribution considère l’effet de la permutation de VNFs dans les chaîne de services et la cinquième contribution explique le placement de VNFs pour les services à valeur ajoutée dans les CDNs
High energy costs and carbon emissions are two significant problems in distributed computing domain, such as distributed clouds and Content Delivery Networks (CDNs). Resource allocation methods (e.g., in form of Virtual Machine (VM) or Virtual Network Function (VNF) placement algorithms) have a direct effect on cost, carbon emission and Quality of Service (QoS). This thesis includes three related parts. First, it targets the problem of resource allocation (i.e., in the form of network aware VM placement algorithms) for distributed clouds and proposes cost and carbon emission efficient resource allocation algorithms for green distributed clouds. Due to the similarity of the network-aware VM placement problem in distributed clouds with a VNF placement problem, the second part of the thesis, getting experience from the first part, proposes a new cost efficient resource allocation algorithm (i.e., VNF placement) for network service provision in data centers and Internet Service Provider (ISP) network. Finally, the last part of the thesis presents new cost efficient resource allocation algorithms (i.e., VNF placement) for value-added service provisioning in NFV-based CDNs
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Doanis, Pavlos. "A Deep Reinforcement Learning Framework for Scalable Slice Orchestration in Beyond 5G Networks". Electronic Thesis or Diss., Sorbonne université, 2024. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2024SORUS100.pdf.

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Cette thèse présente un cadre flexible basé sur l'apprentissage par renforcement des files d'attente pour l'orchestration dynamique des tranches dans les réseaux Beyond 5G, prenant en charge de multiples tranches concurrentes qui couvrent différents domaines technologiques et sont régies par divers accords de niveau de service de bout en bout. Différentes méthodes d'apprentissage par renforcement profond (mono ou multi-agents) sont étudiées pour résoudre les problèmes de complexité d'état et d'action liés à ces problèmes combinatoires, qui rendent l'utilisation d'algorithmes d'apprentissage par renforcement classique impraticable. La performance des schémas proposés est validée par des simulations dans des scénarios de trafic markovien synthétique et de trafic réel
This Thesis introduces a flexible Reinforcement Learning queuing-based framework for dynamic slice orchestration in Beyond 5G networks, supporting multiple concurrent slices that span different technological domains and are governed by diverse end-to-end Service Level Agreements. Different (Deep) Reinforcement Learning methods (single or multi-agent) are investigated to address the state and action complexity hurdles arising in such combinatorial problems, which render the use of "vanilla" Reinforcement Learning algorithms impractical. The performance of the proposed schemes is validated through simulations under both synthetic Markovian traffic and real traffic scenarios
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Cho, Daewoong. "Network Function Virtualization (NFV) Resource Management For Low Network Latency". Thesis, The University of Sydney, 2017. http://hdl.handle.net/2123/17256.

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NFV is an emerging network architecture to increase flexibility and agility within operator's networks by placing virtualized services on demand in Cloud data centers (CDCs). One of the main challenges for the NFV environment is how to efficiently allocate Virtual Network Functions (VNFs) to Virtual Machines (VMs) and how to minimize network latency in the rapidly changing network environments. Although a significant amount of work/research has been already conducted for the generic VNF placement problem and VM migration for efficient resource management in CDCs, network latency among various network components and VNF migration problem have not been comprehensively considered yet to the best of our knowledge. Firstly, to address VNF placement problem, we design a more comprehensive model based on real measurements to capture network latency among VNFs with more granularity to optimize placement of VNFs in CDCs. We consider resource demand of VNFs, resource capacity of VMs and network latency among various network components. Our objectives are to minimize both network latency and lead time (the time to find a VM to host a VNF). Experimental results are promising and indicate that our approach, namely VNF Low-Latency Placement (VNF-LLP), can reduce network latency by up to 64.24% compared with two generic algorithms. Furthermore, it has a lower lead time as compared with the VNF Best-Fit Placement algorithm. Secondly, to address VNF migration problem, we i) formulate the VNF migration problem and ii) develop a novel VNF migration algorithm called VNF Real-time Migration (VNF-RM) for lower network latency in dynamically changing resource availability. As a result of experiments, the effectiveness of our algorithm is demonstrated by reducing network latency by up to 59.45% after latency-aware VNF migrations.
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Ben, Jemaa Fatma. "Design and optimization of next-generation carrier-grade wi-fi networks". Thesis, Paris 6, 2016. http://www.theses.fr/2016PA066226/document.

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Comme le Wi-Fi est devenu de plus en plus important dans les réseaux actuels, ainsi que dans les réseaux du futur, de nouvelles exigences " opérateur " se sont apparues afin de supporter les attentes des utilisateurs et de fournir des réseaux Wi-Fi de haute performance. Dans ce contexte, nous étudions plusieurs problèmes liés à la conception et l'optimisation des réseaux Wi-Fi opérateur de nouvelle génération. Dans la première étape, notre objectif est d'améliorer l'expérience utilisateur Wi-Fi et de lui offrir un accès personnalisé et transparent aux réseaux et services Wi-Fi. Pour cela, nous proposons une extension des trames de gestion IEEE 802.11 pour activer la découverte des services locaux avant l'association Wi-Fi, tout en évitant la surcharge du canal. Nous définissons également un ensemble d'étiquettes de service pour identifier d'une manière standardisée les services les plus connus. Dans la deuxième étape, nous adressons les problèmes liés à l'architecture et la gestion du réseau dans un environnement Wi-Fi opérateur de nouvelle génération. Plus précisément, nous proposons, tout d'abord, une nouvelle architecture Wi-Fi qui exploite les concepts de NFV et du Edge Cloud Computing. Nous visons à travers cette architecture à apporter plus d'agilité et d'adaptabilité et d'améliorer la QoS perçue par l'utilisateur en plaçant des fonctions réseau et certains services à proximité de lui. Pour faire face à certains problèmes de gestion dans cette architecture, nous proposons ensuite des stratégies de placement et de provisionnement des fonctions de réseau virtuelles en s'appuyant sur des exigences de QoS
As Wi-Fi is gaining a lot of momentum in today’s networks as well as in future networks, new carrier-grade requirements are emerging to support future user expectations and provide high-performance Wi-Fi networks. In this context, we investigate several problems surrounding the design and optimization of carrier-grade next-generation Wi-Fi networks. In the first stage, our objective is to improve the Wi-Fi user experience and offer to him a personalized and seamless access to Wi-Fi networks and services. For this, we propose an extension to the IEEE 802.11 management frames to enable venue service discovery prior to Wi-Fi association while avoiding channel overhead. We define also a set of extensible service labels to uniquely and globally identify the most known venue-based services. In the second stage, we deal with network architecture and management issues in next-generation carrier Wi-Fi environment. More specifically, we first propose a novel carrier-managed Wi-Fi architecture that leverages NFV and Edge Cloud Computing concepts. We aim through this architecture to bring more agility and adaptability and improve user perceived QoS by placing network functions and certain services close to end-users. To address some major management issues in this proposed architecture, we then propose placement and provisioning strategies of Virtual Network Functions based on QoS requirements. These strategies can also be applied to any edge-central wireless carrier architecture, since they do not make any assumption about the underlying wireless technology
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Ben, Jemaa Fatma. "Design and optimization of next-generation carrier-grade wi-fi networks". Electronic Thesis or Diss., Paris 6, 2016. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2016PA066226.pdf.

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Comme le Wi-Fi est devenu de plus en plus important dans les réseaux actuels, ainsi que dans les réseaux du futur, de nouvelles exigences " opérateur " se sont apparues afin de supporter les attentes des utilisateurs et de fournir des réseaux Wi-Fi de haute performance. Dans ce contexte, nous étudions plusieurs problèmes liés à la conception et l'optimisation des réseaux Wi-Fi opérateur de nouvelle génération. Dans la première étape, notre objectif est d'améliorer l'expérience utilisateur Wi-Fi et de lui offrir un accès personnalisé et transparent aux réseaux et services Wi-Fi. Pour cela, nous proposons une extension des trames de gestion IEEE 802.11 pour activer la découverte des services locaux avant l'association Wi-Fi, tout en évitant la surcharge du canal. Nous définissons également un ensemble d'étiquettes de service pour identifier d'une manière standardisée les services les plus connus. Dans la deuxième étape, nous adressons les problèmes liés à l'architecture et la gestion du réseau dans un environnement Wi-Fi opérateur de nouvelle génération. Plus précisément, nous proposons, tout d'abord, une nouvelle architecture Wi-Fi qui exploite les concepts de NFV et du Edge Cloud Computing. Nous visons à travers cette architecture à apporter plus d'agilité et d'adaptabilité et d'améliorer la QoS perçue par l'utilisateur en plaçant des fonctions réseau et certains services à proximité de lui. Pour faire face à certains problèmes de gestion dans cette architecture, nous proposons ensuite des stratégies de placement et de provisionnement des fonctions de réseau virtuelles en s'appuyant sur des exigences de QoS
As Wi-Fi is gaining a lot of momentum in today’s networks as well as in future networks, new carrier-grade requirements are emerging to support future user expectations and provide high-performance Wi-Fi networks. In this context, we investigate several problems surrounding the design and optimization of carrier-grade next-generation Wi-Fi networks. In the first stage, our objective is to improve the Wi-Fi user experience and offer to him a personalized and seamless access to Wi-Fi networks and services. For this, we propose an extension to the IEEE 802.11 management frames to enable venue service discovery prior to Wi-Fi association while avoiding channel overhead. We define also a set of extensible service labels to uniquely and globally identify the most known venue-based services. In the second stage, we deal with network architecture and management issues in next-generation carrier Wi-Fi environment. More specifically, we first propose a novel carrier-managed Wi-Fi architecture that leverages NFV and Edge Cloud Computing concepts. We aim through this architecture to bring more agility and adaptability and improve user perceived QoS by placing network functions and certain services close to end-users. To address some major management issues in this proposed architecture, we then propose placement and provisioning strategies of Virtual Network Functions based on QoS requirements. These strategies can also be applied to any edge-central wireless carrier architecture, since they do not make any assumption about the underlying wireless technology
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Chiang, Mao-Jung, i 江茂榮. "Distributed Approach to Adaptive VNF Manager Placement Problem". Thesis, 2019. http://ndltd.ncl.edu.tw/handle/v25ue5.

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碩士
國立交通大學
資訊科學與工程研究所
107
Over the last few years, network function virtualization (NFV) has been developing rapidly in both academia and industry since it promises flexible and scalable network management and orchestration. The European Telecommunications Standards Institute (ETSI) has designed NFV management and orchestration (NFV-MANO) architectural framework for the purpose of managing and orchestrating the network services and their corresponding virtualized network functions (VNFs). In the NFV-MANO, the NFV orchestrator (NFVO) manages the network services and coordinates with the VNF managers (VNFMs) which manage the lifecycle of VNFs. It is a challenging problem to determine the placement of VNFMs that keeps the operational cost down without violating the performance requirements. Existing centralized solution that suffers from scalability and privacy issues, in this paper, we leverage potential game theory to achieve distributed solutions to the VNFM placement problem (MPP). More importantly, our distributed solutions hold the property of self-adaptability. It can adapt the changes in the system like some VNFMs are faulty or the number of VNF instances varies. On the other hand, we also show that the game possesses a potential function and admits a Nash equilibrium (NE). Numerical results show the cost efficiency and adaptability of our approaches.
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Części książek na temat "Placement des VNF"

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Jahedi, Zahra, i Thomas Kunz. "Optimal VNF Placement: Addressing Multiple Min-Cost Solutions". W E-Business and Telecommunications, 1–23. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34866-3_1.

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Bekhit, Mahmoud, Ahmed Fathalla, Esraa Eldesouky i Ahmad Salah. "Multi-objective VNF Placement Optimization with NSGA-III". W Proceedings of the 2023 International Conference on Advances in Computing Research (ACR’23), 481–93. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33743-7_39.

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Zhang, Minli, Hongli Xu, Xingpeng Fan, Da Yao i Liusheng Huang. "Joint Switch Upgrade and VNF Placement for NFV-Based SDNs". W Wireless Algorithms, Systems, and Applications, 87–95. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59019-2_10.

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Pedro, João, i António Eira. "Hybrid Backup Resource Optimization for VNF Placement Over Optical Transport Networks". W Optical Network Design and Modeling, 3–14. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38085-4_1.

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Zeng, Ying, Zhan Shi i Zanhong Wu. "VNF Placement and Routing Algorithm for Energy Saving and QoS Guarantee". W Proceedings of the 9th International Conference on Computer Engineering and Networks, 911–19. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3753-0_89.

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Khemili, Wided, Jalel Eddine Hajlaoui, Mohand Yazid Saidi, Mohamed Nazih Omri i Ken Chen. "Deep Reinforcement Learning for VNF Placement and Chaining of Cloud Network Services". W Advanced Information Networking and Applications, 84–96. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-57870-0_8.

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Solozabal, Ruben, Bego Blanco, Jose Oscar Fajardo, Ianire Taboada, Fidel Liberal, Elisa Jimeno i Javier G. Lloreda. "Design of Virtual Infrastructure Manager with Novel VNF Placement Features for Edge Clouds in 5G". W Engineering Applications of Neural Networks, 669–79. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65172-9_56.

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Masoumi, Maryam, Ignacio de Miguel, Ramón J. Durán Barroso, Soheil Hosseini, Hafiza Kanwal Janjua, Noemí Merayo, Juan Carlos Aguado i Rubén M. Lorenzo. "Efficient Protected VNF Placement and MEC Location Selection for Dynamic Service Provisioning in 5G Networks". W Distributed Computing and Artificial Intelligence, Special Sessions I, 20th International Conference, 448–56. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-38318-2_44.

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Zhong, Youbing, Zhou Zhou, Xuan Liu, Da Li, Meijun Guo, Shuai Zhang, Qingyun Liu i Li Guo. "BPA: The Optimal Placement of Interdependent VNFs in Many-Core System". W Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 305–19. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67540-0_18.

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Marshall, Minda M. B., Simon George Taukeni, Rheinhold Disho Muruti, Gibert Likando, Cynthy Kaliinasho Haihambo, Mathilde Shihako, Chamelle De Silva i Marshall M. "Maximizing Students' Learning Success Through Lab-on-Line". W Addressing Multicultural Needs in School Guidance and Counseling, 262–76. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0319-5.ch012.

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This chapter foregrounds the Lab-On-Line project, a technological innovation developed to enhance visual processing skills, improve memory and vocabulary, and increase reading fluency with the explicit aim of improving comprehension. Thirty (30) 3rd year students at one of the University of Namibia campuses participated in the pilot study. A pre-test was conducted for placement purpose. Subsequently, the selected sample commenced with the Lab-On-Line program that consists of 20 lessons that were carried out twice a week over a period of five months. Thereafter a Standardized Reading Evaluation was performed to determine their language proficiency, reading speed per minute and comprehension ability. Results show that the majority of participating students had improved their perceptual development and reading speed (VPF), cognitive development and comprehension skills (CDF), and relative reading efficiency (AIUF).
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Streszczenia konferencji na temat "Placement des VNF"

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Kim, Sang Il, i Hwa Sung Kim. "A VNF Placement Method based on VNF Characteristics". W 2021 International Conference on Information Networking (ICOIN). IEEE, 2021. http://dx.doi.org/10.1109/icoin50884.2021.9334022.

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Mechtri, Marouen, Chaima Ghribi i Djamal Zeghlache. "VNF Placement and Chaining in Distributed Cloud". W 2016 IEEE 9th International Conference on Cloud Computing (CLOUD). IEEE, 2016. http://dx.doi.org/10.1109/cloud.2016.0057.

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Harris, Dor, i Danny Raz. "Dynamic VNF Placement in 5G Edge Nodes". W 2022 IEEE 8th International Conference on Network Softwarization (NetSoft). IEEE, 2022. http://dx.doi.org/10.1109/netsoft54395.2022.9844093.

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Chiang, Mao-Jung, i Li-Hsing Yen. "Distributed Approach to Adaptive VNF Manager Placement Problem". W 2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS). IEEE, 2019. http://dx.doi.org/10.23919/apnoms.2019.8893005.

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Zhou, Weilin, Yuan Yang, Mingwei Xu i Hao Chen. "Accommodating Dynamic Traffic Immediately: A VNF Placement Approach". W ICC 2019 - 2019 IEEE International Conference on Communications (ICC). IEEE, 2019. http://dx.doi.org/10.1109/icc.2019.8761554.

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Patel, Akanksha, Mythili Vutukuru i Dilip Krishnaswamy. "Mobility-aware VNF placement in the LTE EPC". W 2017 IEEE Conference on Network Function Virtualization and Software-Defined Networks (NFV-SDN). IEEE, 2017. http://dx.doi.org/10.1109/nfv-sdn.2017.8169843.

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Sharma, Gourav Prateek, Wouter Tavernier, Didier Colle i Mario Pickavet. "VNF-AAP: Accelerator-aware Virtual Network Function Placement". W 2019 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN). IEEE, 2019. http://dx.doi.org/10.1109/nfv-sdn47374.2019.9040061.

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Agarwal, Satyam, Francesco Malandrino, Carla-Fabiana Chiasserini i S. De. "Joint VNF Placement and CPU Allocation in 5G". W IEEE INFOCOM 2018 - IEEE Conference on Computer Communications. IEEE, 2018. http://dx.doi.org/10.1109/infocom.2018.8485943.

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Soualah, Oussama, Marouen Mechtri, Chaima Ghribi i Djamal Zeghlache. "Energy Efficient Algorithm for VNF Placement and Chaining". W 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID). IEEE, 2017. http://dx.doi.org/10.1109/ccgrid.2017.84.

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Oechsner, Simon, i Andreas Ripke. "Flexible support of VNF placement functions in OpenStack". W 2015 IEEE Conference on Network Softwarization (NetSoft). IEEE, 2015. http://dx.doi.org/10.1109/netsoft.2015.7116178.

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