VNF Placement in Hybrid NFV Environment: Modeling and Genetic Algorithms

Cao, Jiuyue; An, Wei; Chen, Xin; Han, Yanni; Sun, Jiyan

doi:10.1109/icpads.2016.0105

Cited by 24 publications

(21 citation statements)

References 8 publications

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“…The last one hosts Mininet as the experimental environment. In particular, the selected SFC orchestrator is lightweight since we would like to verify the correctness of the proposed framework rapidly, and we implement three public VNF placement and chaining algorithms (ie, greedy, genetic lgorithm (GA), and CoordVNF) for this orchestrator. Similarly, for the proposed framework, we implement one performance estimation model and two VNF migration algorithms.…”

Section: Evaluation and Resultsmentioning

confidence: 99%

“…Overall, our proposed framework can be regarded as a lightweight plugin for various SFC orchestrators, which targets on estimating the performance of deployed SFCs and providing the “after‐sales service” to the SFCs suffering bad performance. Besides, the proposed framework integrates two VNF migration algorithms and intends to make them in cooperation with other proposed VNF placement algorithms, which include Greedy, genetic algorithm, and CoordVNF). This framework is designed to facilitate the combination of such two kinds of algorithms for the purpose of discovering the potential complementary relationships between them and producing efficient and effective future solutions for SFC management and orchestration.…”

Section: Motivation and Related Workmentioning

confidence: 99%

See 1 more Smart Citation

SDN/NFV‐enabled performance estimation framework for SFC optimization

Wang

Huang

2018

Trans Emerging Tel Tech

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Many network function virtualization management and orchestration frameworks have been proposed to offer an agile and automated service deployment with a set of virtual network function (VNF) placement and chaining algorithms. However, once the services are deployed, less attention is paid to the subsequent supervision on their performance which may be affected by the dynamic changing network conditions. In order to guarantee the performance of services, this article designs and presents a service function chain performance estimation framework, which can be used to complement the network function virtualization orchestrators by providing performance estimation and optimization for the already deployed services. In particular, the performance estimation is implemented based on the min‐plus algebra theory, and the optimization is fulfilled by using two VNF migration approaches on the basis of greedy and load balance, respectively. The proposed framework can (1) evaluate and compare existing VNF placement algorithms by statistics on service performance estimation, (2) estimate and optimize the performance of services by using VNF migration approaches, (3) integrate the VNF placement algorithms and VNF migration approaches, thus producing more efficient future solutions. Finally, this article presents an implementation of the proposed framework and evaluates its effectiveness in an experimental environment.

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Section: Evaluation and Resultsmentioning

confidence: 99%

Section: Motivation and Related Workmentioning

confidence: 99%

SDN/NFV‐enabled performance estimation framework for SFC optimization

Wang

Huang

2018

Trans Emerging Tel Tech

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“…Em [Cao et al 2016], [Carpio et al 2017], [Khebbache et al 2018] e [Tavakoli-Someh et al 2019] são apresentadas soluções baseadas em algoritmos genéticos para executar a tarefa de alocação de servidores durante a integração de um serviço de rede virtualizado. Em especial, a solução em [Cao et al 2016] utiliza soluções baseadas em Multi-Objective Genetic Algorithm (MOGA) e NSGAII para otimizar a seleção de servidores considerando dois objetivos: redução da sobrecarga das conexões entre servidores e balanceamento de carga entre servidores. Já na solução proposta em [Carpio et al 2017], uma heurística genéticá e construída visando a otimização da seleção de servidores em múltiplos centros de processamento.…”

Section: Trabalhos Relacionadosunclassified

Mapeamento Customizado de Serviços de Rede em Múltiplos Domínios Baseado em Heurísticas Genéticas

Fülber-Garcia¹,

Santos²,

Spinosa³

et al. 2020

Anais XXXVIII Simpósio Brasileiro De Redes De Computadores E Sistemas Distribuídos (SBRC 2020)

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A implantação de serviços de rede é um dos principais tarefas do paradigma de Virtualização de Funções de Rede (Network Function Virtualization - NFV). As soluções de mapeamento multi-domínio existentes apresentam diversas restrições em termos do modelos de otimização e métricas de avaliação que adotam. Essa inflexibilidade leva, em última instância, a mapeamentos subotimizados que não correspondem exatamente às necessidades das partes envolvidas no processo, incluindo operadores de rede, clientes, provedores, entre outros. Este trabalho propõe uma nova solução de mapeamento baseada em heurísticas genéticas. Essa nova solução permite a configuração sob demanda dos critérios de avaliação utilizados para a geração de mapeamentos candidatos, sendo capaz de considerar diferentes políticas, limitações tecnológicas e métricas de avaliação definidas de forma específica para cada requisição. Por fim, demonstramos a viabilidade da solução proposta (convergência e tempo de execução) através de um estudo de caso.

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“…The vast majority of studies on VNF placement [5]- [7] implicitly assume that (i) all placement decisions are made by one entity, typically the NFV Orchestrator (NFVO) of the Management and Network Orchestration (MANO) framework [4], [8], and (ii) such entity makes fine-grained decisions about how individual hosts and links are used. However, such a behavior is not the only one included in standards, and is not typical of real-world 5G implementations.…”

Section: Introductionmentioning

confidence: 99%

Getting the Most Out of Your VNFs: Flexible Assignment of Service Priorities in 5G

Malandrino

Chiasserini

2019

2019 IEEE 20th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM)

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Through their computational and forwarding capabilities, 5G networks can support multiple vertical services. Such services may include several common virtual (network) functions (VNFs), which could be shared to increase resource efficiency. In this paper, we focus on the seldom studied VNFsharing problem, and decide (i) whether sharing a VNF instance is possible/beneficial or not, (ii) how to scale virtual machines hosting the VNFs to share, and (iii) the priorities of the different services sharing the same VNF. These decisions are made with the aim to minimize the mobile operator's costs while meeting the verticals' performance requirements. Importantly, we show that the aforementioned priorities should not be determined a priori on a per-service basis, rather they should change across VNFs since such additional flexibility allows for more efficient solutions. We then present an effective methodology called FlexShare, enabling near-optimal VNF-sharing decisions in polynomial time. Our performance evaluation, using real-world VNF graphs, confirms the effectiveness of our approach, which consistently outperforms baseline solutions using per-service priorities.

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VNF Placement in Hybrid NFV Environment: Modeling and Genetic Algorithms

Cited by 24 publications

References 8 publications

SDN/NFV‐enabled performance estimation framework for SFC optimization

SDN/NFV‐enabled performance estimation framework for SFC optimization

Mapeamento Customizado de Serviços de Rede em Múltiplos Domínios Baseado em Heurísticas Genéticas

Getting the Most Out of Your VNFs: Flexible Assignment of Service Priorities in 5G

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