Gerenciamento de IaaS utilizando um Dashboard Inteligente baseado em Ontologia e Feedbacks
Abstract
The accelerated growth in the number of cloud services has made their users develop higher expectations in relation to the quality of service acquired. Considering this fact, this paper purpose to improve the satisfaction of clients still dissatisfied with the service provider, developing a recommendation module based on user feedback. Thus, in this paper a Web platform (Dashboard) was developed that manipulates the IaaSOnt ontology, responsible for reflecting the entire context of the management of an infrastructure as a service (IaaS).
References
Fan, H., Hussain, F., Younas, M., and Hussain, O. (2015). An integrated personalization framework for saas-based cloud services. Future Generation Computer Systems, 53:157–173. cited By 7.
Guo, L., Zheng, X., Ding, C., Mu, D., and Li, Z. (2015). Cloud service recommendation: State of the art and research challenges. pages 761–764. cited By 4.
Jain, R. (1991). The art of computer systems performance analysis techniques for experimental design, measurement, simulation, and modeling. Wiley professional computing. Wiley.
Nakamura, L. H. V. (2017). Mecanismos de Autoconguração e Auto-otimização para arquiteturas virtualizadas que visam a provisão de qualidade de serviço. Tese de doutorado, ICMC USP Universidade de São Paulo, São Carlos, SP.
Neves, Y. T., Nakamura, L. H. V., Prado, P. F., and Santana, M. J. (2014). Famav: Análise In WSCAD comparativa entre ferramentas de gerenciamento de máquinas virtuais. 2014 XV Simpósio em Sistemas Computacionais de Alto Desempenho, volume 15, pages 254–259.
Sun, Y., White, J., Eade, S., and Schmidt, D. C. (2016). Roar: A qos-oriented modeling framework for automated cloud resource allocation and optimization. Journal of Systems and Software, 116(Supplement C):146 – 161.
Zheng, X., Xu, L. D., and Chai, S. (2017). Qos recommendation in cloud services. IEEE Access, 5:5171–5177.
Guo, L., Zheng, X., Ding, C., Mu, D., and Li, Z. (2015). Cloud service recommendation: State of the art and research challenges. pages 761–764. cited By 4.
Jain, R. (1991). The art of computer systems performance analysis techniques for experimental design, measurement, simulation, and modeling. Wiley professional computing. Wiley.
Nakamura, L. H. V. (2017). Mecanismos de Autoconguração e Auto-otimização para arquiteturas virtualizadas que visam a provisão de qualidade de serviço. Tese de doutorado, ICMC USP Universidade de São Paulo, São Carlos, SP.
Neves, Y. T., Nakamura, L. H. V., Prado, P. F., and Santana, M. J. (2014). Famav: Análise In WSCAD comparativa entre ferramentas de gerenciamento de máquinas virtuais. 2014 XV Simpósio em Sistemas Computacionais de Alto Desempenho, volume 15, pages 254–259.
Sun, Y., White, J., Eade, S., and Schmidt, D. C. (2016). Roar: A qos-oriented modeling framework for automated cloud resource allocation and optimization. Journal of Systems and Software, 116(Supplement C):146 – 161.
Zheng, X., Xu, L. D., and Chai, S. (2017). Qos recommendation in cloud services. IEEE Access, 5:5171–5177.
Published
2018-05-06
How to Cite
POLACHINI, Allan; ANDREAZI, Gabriel; ESTRELLA, Júlio; NAKAMURA, Luis H..
Gerenciamento de IaaS utilizando um Dashboard Inteligente baseado em Ontologia e Feedbacks. In: WORKSHOP ON SCIENTIFIC INITIATION AND GRADUATION - BRAZILIAN SYMPOSIUM ON COMPUTER NETWORKS AND DISTRIBUTED SYSTEMS (SBRC), 1. , 2018, Campos do Jordão.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc_estendido.2018.14643.
