Heap Allocator: Uma Política de Alocação de Máquinas Virtuais em Ambiente de Computação em Nuvens baseada em Heap com Prioridade
Resumo
Cloud Computing represents a paradigm that provides computing resources on virtual machines that are grouped and allocated according to customer requests. The increasing seeking for this type of service caused an increasing demand for electric energy. Several types of research aim to meet the demand for resources in Cloud Computing, and at the same time, reduce energy consumption in the data center. Within this context, this paper presents a virtual machine allocation policy called Heap Allocator, which mitigates thepower consumption of the data center. A comparative analysis of the proposed solution with other existing mechanisms showed the proposed solution of the data center power consumption by approximately 1: 5%.
Palavras-chave:
Computação em Nuvem, Virtualização, Centros de Dados
Referências
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Beloglazov, A. and Buyya, R. (2012). Optimal online deterministic algorithms and adap-tive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurr. Comput.:Pract. Exper., 24(13).
Buyya, R., Beloglazov, A., and Abawajy, J. H. (2010). Energy-efficient management of data center resources for cloud computing: a vision, architectural elements, and open challenges. Computing Research Repository.
Calheiros, R. N., Ranjan, R., Beloglazov, A., De Rose, C. A. F., and Buyya, R. (2011). Cloudsim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exper., 41(1).
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Nasim, R. and Kassler, A. J. (2017). A robust tabu search heuristic for vm consolidation under demand uncertainty in virtualized datacenters. In Cluster, Cloud and Grid Com-puting (CCGRID), 2017 17th IEEE/ACM International Symposium on, pages 170-180. IEEE.
Nasim, R., Taheri, J., and Kassler, A. J. (2016). Optimizing virtual machine consolidation in virtualized datacenters using resource sensitivity. In Cloud Computing Technology and Science (CloudCom), 2016 IEEE International Conference on, pages 168-175. IEEE.
Okada, T. K., Vigliotti, A. P. M. D. L. F., Batista, D. M., and vel Lejbman, A. G. (2015). Consolidation of vms to improve energy efficiency in cloud computing environments. pages 150-158.
Park, K. and Pai, V. S. (2006). Comon: A mostly-scalable monitoring system for plane-tlab. SIGOPS Oper. Syst. Rev., 40(1).
Rong, H., Zhang, H., Xiao, S., Li, C., and Hu, C. (2016). Optimizing energy consumption for data centers. Renewable and Sustainable Energy Reviews, 58:674-691.
Sharma, N. K. and Reddy, G. R. M. (2015). Novel energy efficient virtual machine al-location at data center using genetic algorithm. In Signal Processing, Communication and Networking (ICSCN), 2015 3rd International Conference on, pages 1-6. IEEE.
Taheri, J., Zomaya, A. Y., and Kassler, A. (2017). vmbbprofiler: a black-box profiling approach to quantify sensitivity of virtual machines to shared cloud resources. Com-puting, 99(12):1149-1177.
Verma, A., Dasgupta, G., Nayak, T. K., De, P., and Kothari, R. (2009). Server workload analysis for power minimization using consolidation. In Proceedings of the 2009 Con-ference on USENIX Annual Technical Conference, San Diego, California.
Yue, M. (1991). A simple proof of the inequality ffd (l) 11/9 opt (l) + 1, l for the ffd bin-packing algorithm. Acta Mathematicae Applicatae Sinica, 7(4):321-331.
Zhang, Q., Cheng, L., and Boutaba, R. (2010). Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications, 1(1):7-18.
Beloglazov, A. (2013). Energy-efficient management of virtual machines in data centers for cloud computing. PhD thesis, University of Melbourne, Australia.
Beloglazov, A. and Buyya, R. (2012). Optimal online deterministic algorithms and adap-tive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurr. Comput.:Pract. Exper., 24(13).
Buyya, R., Beloglazov, A., and Abawajy, J. H. (2010). Energy-efficient management of data center resources for cloud computing: a vision, architectural elements, and open challenges. Computing Research Repository.
Calheiros, R. N., Ranjan, R., Beloglazov, A., De Rose, C. A. F., and Buyya, R. (2011). Cloudsim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exper., 41(1).
Cormen, T. H. (2009). Introduction to algorithms. MIT press.
Delforge, P. (2014). New study: America's data centers consuming -and wasting -growing amounts of energy. Disponível em: https://www.nrdc.org/experts/pierre-delforge/new-study-americas-data-centers-consuming-and-wasting-growing-amounts-energy. Acesso em: 26-09-2018.
Forbes (2017). Why energy is a big and rapidly growing problem for data centers. Disponível em: https://www.forbes.com/sites/forbestechcouncil/2017/12/15/why-energy-is-a-big-and-rapidly-growing-problem-for-data-centers/3531b0835a30. Acesso em: 21-03-2019.
Goodrich, M. and Tamassia, R. (2013). Estruturas de Dados e Algoritmos em Java -4.ed.:. Bookman Editora.
Jain, R. (1991). The art of computer systems performance analysis -techniques for expe-rimental design, measurement, simulation, and modeling. Wiley.
Lafore, R. (2017). Data structures and algorithms in Java. Sams publishing.
Lago, D. G., Madeira, E. R., and Bittencourt, L. F. (2012). Escalonamento com priori-dade na alocaçao ciente de energia de máquinas virtuais em nuvens. XXX Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 508-521.
Nasim, R. and Kassler, A. J. (2017). A robust tabu search heuristic for vm consolidation under demand uncertainty in virtualized datacenters. In Cluster, Cloud and Grid Com-puting (CCGRID), 2017 17th IEEE/ACM International Symposium on, pages 170-180. IEEE.
Nasim, R., Taheri, J., and Kassler, A. J. (2016). Optimizing virtual machine consolidation in virtualized datacenters using resource sensitivity. In Cloud Computing Technology and Science (CloudCom), 2016 IEEE International Conference on, pages 168-175. IEEE.
Okada, T. K., Vigliotti, A. P. M. D. L. F., Batista, D. M., and vel Lejbman, A. G. (2015). Consolidation of vms to improve energy efficiency in cloud computing environments. pages 150-158.
Park, K. and Pai, V. S. (2006). Comon: A mostly-scalable monitoring system for plane-tlab. SIGOPS Oper. Syst. Rev., 40(1).
Rong, H., Zhang, H., Xiao, S., Li, C., and Hu, C. (2016). Optimizing energy consumption for data centers. Renewable and Sustainable Energy Reviews, 58:674-691.
Sharma, N. K. and Reddy, G. R. M. (2015). Novel energy efficient virtual machine al-location at data center using genetic algorithm. In Signal Processing, Communication and Networking (ICSCN), 2015 3rd International Conference on, pages 1-6. IEEE.
Taheri, J., Zomaya, A. Y., and Kassler, A. (2017). vmbbprofiler: a black-box profiling approach to quantify sensitivity of virtual machines to shared cloud resources. Com-puting, 99(12):1149-1177.
Verma, A., Dasgupta, G., Nayak, T. K., De, P., and Kothari, R. (2009). Server workload analysis for power minimization using consolidation. In Proceedings of the 2009 Con-ference on USENIX Annual Technical Conference, San Diego, California.
Yue, M. (1991). A simple proof of the inequality ffd (l) 11/9 opt (l) + 1, l for the ffd bin-packing algorithm. Acta Mathematicae Applicatae Sinica, 7(4):321-331.
Zhang, Q., Cheng, L., and Boutaba, R. (2010). Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications, 1(1):7-18.
Publicado
16/09/2019
Como Citar
MAGALHÃES DE CARVALHO, Matheus ; VIEIRA, Francisco Mardônio; DE MATOS, Filipe Fernandes dos Santos; LOPES GOMES, Rafael ; CELESTINO JÚNIOR, Joaquim .
Heap Allocator: Uma Política de Alocação de Máquinas Virtuais em Ambiente de Computação em Nuvens baseada em Heap com Prioridade. In: WORKSHOP EM CLOUDS E APLICAÇÕES (WCGA), 17. , 2019, Gramado.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 57-70.
DOI: https://doi.org/10.5753/wcga.2019.7594.
