13 research outputs found

    Latency and Availability Driven VNF Placement in a MEC-NFV Environment

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    Multi-access Edge Computing (MEC) is gaining momentum as it is considered as one of the enablers of 5G ultra-Reliable Low-Latency Communications (uRLLC) services. MEC deploys computation resources close to the end user, enabling to reduce drastically the end-to-end latency. ETSI has recently leveraged the MEC architecture to run all MEC entities, including MEC applications, as Virtual Network Functions (VNF) in a Network Functions Virtualization (NFV) environment. This evolution allows taking advantage of the mature architecture and the enabling tools of NFV, including the potential to apply a variety of service-tailored function placement algorithms. However, the latter need to be carefully designed in case of MEC applications such as uRLLC, where service access latency is critical. In this paper, we propose a novel placement scheme applicable to a MEC in NFV environment. In particular, we propose a formulation of the problem of VNF placement tailored to uRLLC as an optimization problem of two conflicting objectives, namely minimizing access latency and maximizing service availability. To deal with the complexity of the problem, we propose a Genetic Algorithm to solve it, which we compare with a CPLEX implementation of our model. Our numerical results show that our heuristic algorithm runs efficiently and produces solutions that approximate well the optimal, reducing latency and providing a highly-available service.This work has been partially supported by the European Union’s H2020 5G-Transformer Project (grant no. 761536

    Cost and availability aware resource allocation and virtual function placement for CDNaaS provision

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    We address the fundamental tradeoff between deployment cost and service availability in the context of on-demand content delivery service provision over a telecom operator's network functions virtualization infrastructure. In particular, given a specific set of preferences and constraints with respect to deployment cost, availability and computing resource capacity, we provide polynomial-time heuristics for the problem of jointly deriving an appropriate assignment of computing resources to a set of virtual instances and the placement of the latter in a subset of the available physical hosts. We capture the conflicting criteria of service availability and deployment cost by proposing a multi-objective optimization problem formulation. Our algorithms are experimentally shown to outperform state-of-the-art solutions in terms of both execution time and optimality, while providing the system operator with the necessary flexibility to balance between conflicting objectives and reflect the relevant preferences of the customer in the produced solutions.This work was supported in part by the French FUI-18 DVD2C project and by the European Union’s Horizon 2020 research and innovation program under the 5G-Transformer project (grant no. 761536)

    Balancing between cost and availability for CDNaaS resource placement

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    Les réseaux de diffusion de contenu

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    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.Le but de cette thèse est d’étudier et d’évaluer le rĂŽle de la virtualisation des réseau de diffusion de contenu. Nous proposons une implĂ©mentation 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

    Cost and availability aware resource allocation and virtual function placement for CDNaaS provision

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    Latency and availability driven VNF placement in a MEC-NFV environment

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    QoE-aware computing resource allocation for CDN-as-a-service provision

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    Les réseaux de diffusion de contenu

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    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.Le but de cette thèse est d’étudier et d’évaluer le rĂŽle de la virtualisation des réseau de diffusion de contenu. Nous proposons une implĂ©mentation 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

    CDN-as-a-Service Provision over a Telecom Operator's Cloud

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    International audienceWe present the design and implementation of a Content-Delivery-Network-as-a-Service (CDNaaS) architecture, which allows a telecom operator to open up its cloud infrastructure for content providers to deploy virtual CDN instances on demand, at regions where the operator has presence. Using northbound REST APIs, content providers can express performance requirements and demand specifications, which are translated to an appropriate service placement on the underlying cloud substrate. Our architecture is extensible, supporting various different CDN flavors, and, in turn, different schemes for cloud resource allocation and management. In order to decide on the latter in an optimal manner from an infrastructure cost and a service quality perspective, knowledge of the performance capabilities of the underlying technologies and compute resources is critical. Therefore, to gain insight which can be applied to the design of such mechanisms, but also with further implications on service pricing and SLA design, we carry out a measurement campaign to evaluate the capabilities of key enabling technologies for CDNaaS provision. In particular, we focus on virtualization and containerization technologies for implementing virtual CDN functions to deliver a generic HTTP service, as well as an HTTP video streaming one, empirically capturing the relationship between performance and service workload, both from a system operator and a user-centric viewpoint
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