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.
Em uma Nuvem de Dispositivos ou Nuvem das Coisas, os recursos disponíveis na Nuvem nem sempre são capazes de atender às restrições de latência de aplicações críticas devido à grande distância entre a Nuvem e os dispositivos que originam os dados. A adoção de computação na Borda pode auxiliar a Nuvem a fornecer serviços que atendam a tais requisitos temporais. Contudo, dois fatores afetam o desempenho no nível de Borda: a heterogeneidade das aplicações e a incerteza da taxa de chegada das requisições. Neste contexto, propomos dois mecanismos: o Gerenciador de Fila, que redistribui as requisições das filas dos Nós de Borda, permitindo reduzir o tempo de espera de uma requisição; e o Alocador de Recursos, que possibilita a colaboração entre os Nós de Borda por meio de encaminhamento de requisições.
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