Arcabouço de um Sistema Inteligente de Monitoramento para Cloud Slices
Resumo
Esse trabalho se baseia no paradigma slice-as-a-service proposto no projeto Novel Enablers for Cloud Slicing (NECOS). Assumindo fatias de rede (slices) fim-a-fim, compostas por recursos de múltiplos provedores de infraestrutura, esse trabalho estuda um sistema inteligente de monitoramento capaz de selecionar dinamicamente métricas que melhor atendam às necessidades de gerenciamento dos slices, mantendo a precisão. Basicamente, deseja-se evitar o tráfego de dados desnecessários extraídos dos diferentes provedores de infraestrutura, entregando um conjunto essencial de dados para as funções de gerenciamento. Um primeiro experimento em nosso protótipo é apresentado e alguns dos benefícios da seleção de características já podem ser observados.
Palavras-chave:
Virtualização, Computação em Nuvem, Fatiamento de Redes
Referências
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Kächele, S., Spann, C., Hauck, F. J., and Domaschka, J. (2013). Beyond iaas and paas: An extended cloud taxonomy for computation, storage and networking. In 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing, pages 75-82.
Necos (2017a). D3.1: Necos system architecture and platform specification. v1. http: //www.maps.upc.edu/public/NECOS%20D3.1%20final.pdf. Acessado em: 12/03/2019.
Necos (2017b). D5.1: Architectural update, monitoring and control policies fra-meworks. http://www.maps.upc.edu/public/D5.1_final.pdf. Aces-sado em: 12/03/2019.
Necos (2017c). Motivation and vision. http://www.h2020-necos.eu/ motivation-and-vision/. Acessado em: 04/12/2018.
Pasquini, R. and Stadler, R. (2017). Learning end-to-end application qos from openflow switch statistics. In 2017 IEEE Conference on Network Softwarization (NetSoft), pages 1-9.
SAR (2018). Sar. https://linux.die.net/man/1/sar. Acessado em: 21/12/2018.
Voldemort (2016). Voldemort Project. http://www.project-voldemort.com/ voldemort/. Online; acessado em 20/03/2019.
Yang, K., Yoon, H., and Shahabi, C. (2005). A supervised feature subset selection te-chnique for multivariate time series. In In Proceedings of the Workshop on Feature Selection for Data Mining: Interfacing Machine Learning with Statistics, 92-101.
Zhu, X. (2005). Semi-supervised learning literature survey. https: //minds.wisconsin.edu/bitstream/handle/1793/60444/TR1530. pdf?sequence=1. Acessado em: 17/03/2019.
Ceilometer (2018). Ceilometer. https://docs.openstack.org/ceilometer/ latest/. Acessado em: 21/12/2018.
Chandrashekar, G. and Sahin, F. (2014). A survey on feature selection methods. Compu-ters & Electrical Engineering, 40(1):16 -28. 40th-year commemorative issue.
Encoding.com (2016). Mpeg-dash an overview. https://www.encoding.com/ mpeg-dash/. Acessado em: 17/03/2019.
Fulmari1, A. and Chandak, M. B. (2013). A survey on supervised learning for word sense disambiguation. https://pdfs.semanticscholar.org/ 58bb/8f4b9a0e7257ca15555e505e9fd35992f66c.pdf. Acessado em: 17/03/2019.
INRIA (2018). Scikit learn. https://scikit-learn.org/stable/. Acessado em: 12/12/2018.
John, G. H., Kohavi, R., and Pfleger, K. (1994). Irrelevant features and the subset selection problem. In ICML 1994.
Kira, K. and A. Rendell, L. (1992). The feature selection problem: Traditional methods and a new algorithm. In The Feature Selection Problem: Traditional Methods and a New Algorithm., pages 129-134.
Kochie, B. (2018). Prometheus node-exporter. https://github.com/ prometheus/node_exporter/releases. Acessado em: 06/02/2019.
Kächele, S., Spann, C., Hauck, F. J., and Domaschka, J. (2013). Beyond iaas and paas: An extended cloud taxonomy for computation, storage and networking. In 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing, pages 75-82.
Necos (2017a). D3.1: Necos system architecture and platform specification. v1. http: //www.maps.upc.edu/public/NECOS%20D3.1%20final.pdf. Acessado em: 12/03/2019.
Necos (2017b). D5.1: Architectural update, monitoring and control policies fra-meworks. http://www.maps.upc.edu/public/D5.1_final.pdf. Aces-sado em: 12/03/2019.
Necos (2017c). Motivation and vision. http://www.h2020-necos.eu/ motivation-and-vision/. Acessado em: 04/12/2018.
Pasquini, R. and Stadler, R. (2017). Learning end-to-end application qos from openflow switch statistics. In 2017 IEEE Conference on Network Softwarization (NetSoft), pages 1-9.
SAR (2018). Sar. https://linux.die.net/man/1/sar. Acessado em: 21/12/2018.
Voldemort (2016). Voldemort Project. http://www.project-voldemort.com/ voldemort/. Online; acessado em 20/03/2019.
Yang, K., Yoon, H., and Shahabi, C. (2005). A supervised feature subset selection te-chnique for multivariate time series. In In Proceedings of the Workshop on Feature Selection for Data Mining: Interfacing Machine Learning with Statistics, 92-101.
Zhu, X. (2005). Semi-supervised learning literature survey. https: //minds.wisconsin.edu/bitstream/handle/1793/60444/TR1530. pdf?sequence=1. Acessado em: 17/03/2019.
Publicado
25/09/2019
Como Citar
MARQUES, Gustavo Silveira; GUARDIEIRO PEREIRA REZENDE, Aryadne ; CUNHA, Ian Resende; FIALHO, Raquel ; PASQUINI, Rafael .
Arcabouço de um Sistema Inteligente de Monitoramento para Cloud Slices. In: WORKSHOP DE TEORIA, TECNOLOGIAS E APLICAÇÕES DE SLICING PARA INFRAESTRUTURAS SOFTWARIZADAS (WSLICE), 1. , 2019, Gramado.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 56-68.
DOI: https://doi.org/10.5753/wslice.2019.7722.
