Implantação e Avaliação de um Sistema de Monitoramento de Recursos Computacionais de Cluster: um enfoque em desenvolvimento sustentável
Resumo
A implantação do sistema de monitoramento computacional aprimora a gestão estratégica de recursos computacionais. A literatura apresenta diversas ferramentas para monitoramento, porém, há uma lacuna em relação a sistemas que utilizam o gerenciador de recursos OpenPBS. Este trabalho visa contribuir para o desenvolvimento sustentável da Amazônia legal implementando um sistema de monitoramento de recursos computacionais como ferramenta precursora para introdução do green computing no cluster alinhada ao Plano de Desenvolvimento Institucional de uma Instituição Federal de Ensino Superior localizada no coração da região Amazônica. A implantação bem-sucedida do monitoramento contribui para uma visão mais abrangente do sistema de alto desempenho, guiando a gestão estratégica deste importante ativo.Referências
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Sorkunlu, N., Chandola, V., and Patra, A. (2017). Tracking system behavior from resource usage data. In 2017 IEEE International Conference on Cluster Computing (CLUSTER), pages 410–418. IEEE.
Stanisic, L. and Reuter, K. (2020). Mpcdf hpc performance monitoring system: Enabling insight via job-specific analysis. In Euro-Par 2019: Parallel Processing Workshops: Euro-Par 2019 International Workshops, Göttingen, Germany, August 26–30, 2019, Revised Selected Papers 25, pages 613–625. Springer.
Chi, W. and Zhou, W. (2019). A realtime monitoring method for cluster system running state based on network. In Journal of Physics: Conference Series, number 2. IOP Publishing.
Cocaña-Fernández, A., Guiote, E. S. J., Ranilla, J., and Sánchez, L. (2019). Improving eecluster to optimize the carbon footprint and operating costs of hpc clusters. In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pages 1–6. IEEE.
Kashin, D. and Voevodin, V. (2023). Verifying the correctness of hpc performance monitoring data. In International Conference on Parallel Computing Technologies, pages 197–208. Springer.
Kunz, P. (2022). Hpc job-monitoring with slurm, prometheus and grafana.
Li, B., Basu Roy, R., Wang, D., Samsi, S., Gadepally, V., and Tiwari, D. (2023). Toward sustainable hpc: Carbon footprint estimation and environmental implications of hpc systems. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pages 1–15.
Mohapatra, S. K., Nayak, P., Mishra, S., and Bisoy, S. K. (2019). Green computing: a step towards eco-friendly computing. In Emerging trends and applications in cognitive computing, pages 124–149. IGI global.
Peffers, K., Tuunanen, T., Gengler, C. E., Rossi, M., Hui, W., Virtanen, V., and Bragge, J. (2020). Design science research process: A model for producing and presenting information systems research.
Saputra, M. Y. E., Arief, S. N., Wijayaningrum, V. N., Syaifudin, Y. W., et al. (2024). Real-time server monitoring and notification system with prometheus, grafana, and telegram integration. In 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS), pages 1808–1813. IEEE.
Sorkunlu, N., Chandola, V., and Patra, A. (2017). Tracking system behavior from resource usage data. In 2017 IEEE International Conference on Cluster Computing (CLUSTER), pages 410–418. IEEE.
Stanisic, L. and Reuter, K. (2020). Mpcdf hpc performance monitoring system: Enabling insight via job-specific analysis. In Euro-Par 2019: Parallel Processing Workshops: Euro-Par 2019 International Workshops, Göttingen, Germany, August 26–30, 2019, Revised Selected Papers 25, pages 613–625. Springer.
Publicado
23/10/2024
Como Citar
EMERIQUE, Vitor Torres; LOBATO, Fábio Manoel França; SILVA, Marcelino da Silva.
Implantação e Avaliação de um Sistema de Monitoramento de Recursos Computacionais de Cluster: um enfoque em desenvolvimento sustentável. In: WORKSHOP DE INICIAÇÃO CIENTÍFICA - SIMPÓSIO EM SISTEMAS COMPUTACIONAIS DE ALTO DESEMPENHO (SSCAD), 25. , 2024, São Carlos/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 113-120.
DOI: https://doi.org/10.5753/sscad_estendido.2024.244369.
