Behavioral Analysis for Virtualized Network Functions : A SOM-based Approach

In this paper, we tackle the problem of detecting anomalous behaviors in a virtualized infrastructure for network function virtualization, proposing to use self-organizing maps for analyzing historical data available through a data center. We propose a joint analysis of system-level metrics, mostly related to resource consumption patterns of the hosted virtual machines, as available through the virtualized infrastructure monitoring system, and the application-level metrics published by individual virtualized network functions through their own monitoring subsystems. Experimental results, obtained by processing real data from one of the NFV data centers of the Vodafone network operator, show that our technique is able to identify specific points in space and time of the recent evolution of the monitored infrastructure that are worth to be investigated by a human operator in order to keep the system running under expected conditions

Behavioral analysis for virtualized network functions: A som-based approach

In this paper, we tackle the problem of detecting anomalous behaviors in a virtualized infrastructure for network function virtualization, proposing to use self-organizing maps for analyzing historical data available through a data center. We propose a joint analysis of system-level metrics, mostly related to resource consumption patterns of the hosted virtual machines, as available through the virtualized infrastructure monitoring system, and the application-level metrics published by individual virtualized network functions through their own monitoring subsystems. Experimental results, obtained by processing real data from one of the NFV data centers of the Vodafone network operator, show that our technique is able to identify specific points in space and time of the recent evolution of the monitored infrastructure that are worth to be investigated by a human operator in order to keep the system running under expected conditions

Predictive auto-scaling with OpenStack Monasca

Cloud auto-scaling mechanisms are typically based on reactive automation rules that scale a cluster whenever some metric, e.g., the average CPU usage among instances, exceeds a predefined threshold. Tuning these rules becomes particularly cumbersome when scaling-up a cluster involves non-negligible times to bootstrap new instances, as it happens frequently in production cloud services. To deal with this problem, we propose an architecture for auto-scaling cloud services based on the status in which the system is expected to evolve in the near future. Our approach leverages on time-series forecasting techniques, like those based on machine learning and artificial neural networks, to predict the future dynamics of key metrics, e.g., resource consumption metrics, and apply a threshold-based scaling policy on them. The result is a predictive automation policy that is able, for instance, to automatically anticipate peaks in the load of a cloud application and trigger ahead of time appropriate scaling actions to accommodate the expected increase in traffic. We prototyped our approach as an open-source OpenStack component, which relies on, and extends, the monitoring capabilities offered by Monasca, resulting in the addition of predictive metrics that can be leveraged by orchestration components like Heat or Senlin. We show experimental results using a recurrent neural network and a multi-layer perceptron as predictor, which are compared with a simple linear regression and a traditional non-predictive auto-scaling policy. However, the proposed framework allows for the easy customization of the prediction policy as needed

Uma plataforma NFV-MANO para suporte e orquestração de serviços de rede virtualizados em nuvem CloudStack

Orientador: Elias Procópio Duarte JúniorDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 05/02/2021Inclui referências: p. 62-65Área de concentração: Ciência da ComputaçãoResumo: A Virtualização de Funções de Rede (Network Functions Virtualization - NFV) permite que funções de rede tradicionalmente executadas em hardware especializado sejam implementadas em software e instanciadas como VNFs (Virtualized Network Functions) sobre hardware de propósito geral. Além do aumento da flexibilidade, uma consequência é a redução dos custos operacionais (OPEX) e de capital (CAPEX). A arquitetura de referência NFV-MANO (NFV - Management and Orchestration) é constituída por um conjunto de especificações para a construção de soluções NFV interoperáveis e tem sido amplamente adotada, sendo utilizada por diversas plataformas NFV. Entretanto, quase todas as soluções NFV atuais oferecem suporte a uma plataforma de nuvem em particular: o OpenStack. Por outro lado, o Apache CloudStack, uma das principais plataformas de nuvem da atualidade, tem sido superficialmente explorado no contexto de NFV. Este trabalho apresenta o Vines (Vines Is an NFV-MANO Extensible Solution), uma plataforma NFV-MANO integrada ao CloudStack e publicamente disponibilizada como software de código aberto. O Vines inclui funcionalidades diferenciais em relação a outras plataformas NFV, mesmo considerando as que utilizam o OpenStack como base. Estas funcionalidades têm como objetivo cobrir limitações de gerenciamento de VNFs frequentemente presentes nas atuais plataformas. Por exemplo, tais plataformas oferecem um conjunto reduzido de operações de gerência de VNFs e não disponibilizam suporte para plataformas de execução de VNFs. Neste sentido, o Vines possui uma arquitetura holística de gerência de VNFs, que o permite lidar com funções de rede heterogêneas e suportar distintas plataformas de execução de VNFs. O Vines disponibiliza um amplo conjunto de operações de gerenciamento do ciclo de vida de VNFs, o que inclui a instanciação, atualização e remoção de VNFs; instalação, configuração, inicialização e terminação de funções de rede; e ainda a recuperação automática de VNFs falhas e escala automática de VNFs. Além disso, o Vines possibilita a orquestração e gerência de serviços de rede complexos, implementados como Service Function Chains (SFC) que são composições de múltiplas VNFs. A solução proposta foi avaliada empiricamente, comparando (quando possível) o CloudStack/Vines com o OpenStack/Tacker. Os resultados comprovam a eficácia do Vines, ao demonstrar sua capacidade de realizar todas as operações para as quais foi projetado, e indicam um nível satisfatório de eficiência, com desempenho similar ao do OpenStack/Tacker. Palavras-chave: Virtualização de Funções de Rede. Serviços de Rede. Computação em Nuvem.Abstract: Network Functions Virtualization (NFV) allows the implementation of network functions in software that can be executed on general purpose hardware in the network core. Complex network services can be built as compositions of VNFs (Virtualized Network Functions) and are known as SFCs (Service Function Chains). The advantages of NFV technology are manyfold: in addition to increasing the flexibility, NFV technology promotes a reduction of costs, including both capital and operating expenditures (CAPEX and OPEX). The NFV-MANO (NFV - Management and Orchestration) reference architecture is a set of specifications that has been proposed to allow different providers to build interoperable NFV solutions. NFV-MANO has been widely adopted, several NFV platforms are MANO-compliant. Curiously, almost all current NFV solutions support a single cloud platform: OpenStack. In particular, Apache CloudStack, one of the most widely adopted cloud platforms worldwide, has been barely explored in the context NFV. In this work we present Vines (Vines Is an NFV-MANO Extensible Solution), a MANO-compliant platform integrated with CloudStack and publicly available as open source software. Besides being the first NFV platform that is native to CloudStack, Vines includes several functionalities that fill gaps left by other platforms. In particular, Vines presents a holistic VNF management architecture that features a comprehensive set of VNF lifecycle operations for deploying, updating, and deleting VNFs and supports heterogeneous network functions and different VNF execution platforms. A fully MANO-compliant EMS (Element Management System) facilitates the installation, configuration, initialization, and termination of network functions, also allowing VNF scaling and automatic recovery after failures. Vines also enables the orchestration and management of complex network services implemented as SFCs. The proposed solution was evaluated empirically, also comparing CloudStack/Vines with OpenStack/Tacker whenever possible. Results show the effectiveness of Vines, confirming its ability to perform all the operations for which it was designed with a performance level similar to that of OpenStack/Tacker. Keywords: Network Functions Virtualization. Network Services. Cloud Computing