Relações do Consumo Energético nas Execuções de Tarefas em Computação em Nuvem Verde
Abstract
Cloud computing is already part of the daily lives of people and companies, as well as environmental concerns. The union of these two realities provides an opportunity for the emergence of Green Cloud Computing with new proposals, approaches and metrics in order to make datacenters more efficient, mainly in terms of energy, in order to reduce CO2 emissions and the environmental impact. Knowing the metrics that can be used to measure the energy cost and environmental impact of datacenters as well as the main categories and areas of action are fundamental bases in Green Computing. This article presents a study of the relationship between energy consumption and execution times, architecture and costs of a cloud environment. Through the use of simulators, it was possible to prove the improvement in energy efficiency above 60% and reduction in processing times by at least 29%, with the adoption of algorithms, of complex problems, in the scheduling of Virtual Machines.
References
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Meyer, V., Krindges, R., Ferreto, T. C., De Rose, C. A., and Hessel, F. (2018). Simulators usage analysis to estimate power consumption in cloud computing environments. In 2018 Symposium on High Performance Computing Systems (WSCAD), pages 70-76. IEEE.
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Silva Filho, M. C., Oliveira, R. L., Monteiro, C. C., Inácio, P. R., and Freire, M. M. (2017). Cloudsim plus: A cloud computing simulation framework pursuing software engineering principles for improved modularity, extensibility and correctness. In 2017 IFIP/IEEE symposium on integrated network and service management (IM), pages 400-406. IEEE.
Wadhwa, M., Goel, A., Choudhury, T., and Mishra, V. P. (2019). Green cloud computing-a greener approach to it. In 2019 international conference on computational intelligence and knowledge economy (ICCIKE), pages 760-764. IEEE.
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Barbierato, E., Gribaudo, M., Iacono, M., and Jakóbik, A. (2019). Exploiting cloudsim in a multiformalism modeling approach for cloud based systems. Simulation Modelling Practice and Theory, 93:133-147.
Chopra, J., Rangarajan, V., and Sen, R. (2021). Sustainable energy technologies and assessments.
CLOUDSIM (2016). Full-featured and fully documented cloud simulation framework. Disponível em: http://cloudsimplus.org/. Acesso em: Junho 2021.
Farahnakian, F., Ashraf, A., Pahikkala, T., Liljeberg, P., Plosila, J., Porres, I., and Tenhunen, H. (2015). Using ant colony system to consolidate vms for green cloud computing. IEEE Transactions on Services Computing, 8(2):187-198.
Gade, A. (2018). Survey on energy efficient cloud: A novel approach towards green computing. HELIX.
Jena, S. R., Shanmugam, R., Saini, K., and Kumar, S. (2020). Cloud computing tools: inside views and analysis. Procedia Computer Science, 173:382-391.
Junior, T. J. T. and Bruschi, S. (2020). Epcsac-extensible platform for cloud scheduling algorithm comparison. In Anais Estendidos do XXI Simpósio em Sistemas Computacionais de Alto Desempenho, pages 46-53. SBC.
Khan, R. and Khan, S. U. (2016). Achieving energy saving through proxying applications on behalf of idle devices. Procedia Computer Science, 83:187-194.
Makaratzis, A. T., Giannoutakis, K. M., and Tzovaras, D. (2018). Energy modeling in cloud simulation frameworks. Future Generation Computer Systems, 79:715-725.
Mandal, A. K. and Dehuri, S. (2019). A survey on ant colony optimization for solving some of the selected np-hard problem. In International Conference on Biologically Inspired Techniques in Many-Criteria Decision Making, pages 85-100. Springer.
Masdari, M. and Zangakani, M. (2020). Green cloud computing using proactive virtual machine placement: challenges and issues. Journal of Grid Computing, 18(4):727-759.
Meyer, V., Krindges, R., Ferreto, T. C., De Rose, C. A., and Hessel, F. (2018). Simulators usage analysis to estimate power consumption in cloud computing environments. In 2018 Symposium on High Performance Computing Systems (WSCAD), pages 70-76. IEEE.
Radu, L.-D. (2017). Green cloud computing: A literature survey. Symmetry, 9(12):295.
Saha, B. (2018). Green computing: current research trends. International Journal of Computer Sciences and Engineering, 6(3):467-469.
Silva Filho, M. C., Oliveira, R. L., Monteiro, C. C., Inácio, P. R., and Freire, M. M. (2017). Cloudsim plus: A cloud computing simulation framework pursuing software engineering principles for improved modularity, extensibility and correctness. In 2017 IFIP/IEEE symposium on integrated network and service management (IM), pages 400-406. IEEE.
Wadhwa, M., Goel, A., Choudhury, T., and Mishra, V. P. (2019). Green cloud computing-a greener approach to it. In 2019 international conference on computational intelligence and knowledge economy (ICCIKE), pages 760-764. IEEE.
Yang, J., Xiao, W., Jiang, C., Hossain, M. S., Muhammad, G., and Amin, S. U. (2018). Ai-powered green cloud and data center. IEEE Access, 7:4195-4203.
Zong, Z. (2020). An improvement of task scheduling algorithms for green cloud computing. In 2020 15th International Conference on Computer Science & Education (ICCSE), pages 654-657. IEEE.
Published
2022-09-28
How to Cite
REIS, Thiago Nelson Faria dos; TEIXEIRA, Mário Meireles; SOARES NETO, Carlos de Salles.
Relações do Consumo Energético nas Execuções de Tarefas em Computação em Nuvem Verde. In: REGIONAL SCHOOL ON COMPUTING OF CEARÁ, MARANHÃO, AND PIAUÍ (ERCEMAPI), 10. , 2022, São Luís/MA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 119-128.
DOI: https://doi.org/10.5753/ercemapi.2022.226178.
