Uma Abordagem Baseada no Consumo de CPU e RAM para a Eficiência Energética em Centros de Dados para Computação em Nuvem
Resumo
A computação em nuvem tem levado os sistemas distribuídos a um novo patamar, oferecendo recursos computacionais de forma virtualizada, flexível, robusta e escalar. Essas vantagens, no entanto, surgem juntamente com um alto consumo de energia nos centros de dados, ambientes que podem ter até centenas de milhares de servidores. Existem muitas propostas para alcançar eficiência energética em centros de dados voltados para computação em nuvem. Entretanto, muitas propostas consideram apenas o consumo proveniente do uso de CPU, além de empregarem definições de SLA dependentes do domínio da aplicação. Neste trabalho, propomos duas novas abordagens para melhorar a eficiência energética desses ambientes. As abordagens se baseiam no consumo proveniente do uso de CPU e de memória conjuntamente. Implementamos e validamos ambas as propostas no simulador CloudSim e comparamos os resultados com outras soluções consideradas estado da arte. Nossas propostas reduzem o consumo de energia em até 33% quando comparadas com outras abordagens. Elas também diminuem a violação de SLA em até 90%, mesmo quando este é definido de forma genérica.
Palavras-chave:
computação em nuvem, centro de dados, eficiência energética, cumprimento de SLA, consolidação de máquinas virtuais
Referências
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X. Fan, W.-D. Weber, and L. A. Barroso, “Power provisioning for a warehouse-sized computer,” in Proceedings of the 34th annual international symposium on Computer architecture, 2007, pp. 13–23.
L. Barroso and U. Holzle, “The Case for Energy-Proportional Computing,” Computer, vol. 40, no. 12, pp. 33–37, 2007.
L. Benini, A. Bogliolo, and G. De Micheli, “A survey of design techniques for system-level dynamic power management,” Very Large Scale Integration (VLSI) Systems, IEEE Transactions on, vol. 8, no. 3, pp. 299–316, 2000.
C.-H. Hwang and A. C.-H. Wu, “A predictive system shutdown method for energy saving of event-driven computation,” ACM Trans. Des. Autom. Electron. Syst, vol. 5, no. 2, pp. 226–241, 2000.
M. Weiser, B. Welch, A. Demers, and S. Shenker, “Scheduling for Reduced CPU Energy,” Mobile Computing, vol. 353, pp. 449–471, 1996.
A. Wierman, L. L. H. Andrew, and A. Tang, “Power-aware speed scaling in processor sharing systems: Optimality and robustness,” Perform. Eval., vol. 69, no. 12, pp. 601–622, 2012.
D. Kusic, J. O. Kephart, J. E. Hanson, N. Kandasamy, and G. Jiang, “Power and performance management of virtualized computing environments via lookahead control,” Cluster Computing, vol. 12, no. 1, pp. 1–15, 2009.
A. Verma, P. Ahuja, and A. Neogi, “pMapper: power and migration cost aware application placement in virtualized systems,” in Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware, 2008, pp. 243–264.
Q. Zhang, M. F. Zhani, R. Boutaba, and J. L. Hellerstein, “HARMONY: Dynamic Heterogeneity-Aware Resource Provisioning in the Cloud,” in Proceedings of the 33rd International Conference on Distributed Computing Systems (ICDCS), 2013.
A. Beloglazov and R. Buyya, “Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers,” Concurr. Comput. : Pract. Exper., vol. 24, no. 13, pp. 1397–1420, 2012.
K. Lim, J. Chang, T. Mudge, P. Ranganathan, S. K. Reinhardt, and T. F. Wenisch, “Disaggregated memory for expansion and sharing in blade servers,” SIGARCH Comput. Archit. News, vol. 37, no. 3, pp. 267–278, 2009.
R. Nathuji and K. Schwan, “VirtualPower: coordinated power management in virtualized enterprise systems,” in Proceedings of twenty-first ACM SIGOPS symposium on Operating systems principles, 2007, pp. 265–278.
H. David, C. Fallin, E. Gorbatov, U. R. Hanebutte, and O. Mutlu, “Memory power management via dynamic voltage/frequency scaling,” in Proceedings of the 8th ACM international conference on Autonomic computing, 2011, pp. 31–40.
SPEC, “SPECpower ssj2008 benchmark,” http://www.spec.org/powerssj2008/, 2013, Último acesso: 15-julho-2013.
C. Clark, K. Fraser, S. Hand, J. G. Hansen, E. Jul, C. Limpach, I. Pratt, and A. Warfield, “Live migration of virtual machines,” in Proceedings of the 2nd conference on Symposium on Networked Systems Design & Implementation-Volume 2, 2005, pp. 273–286.
CLOUDSLab, “CloudSim: A Framework For Modeling And Simulation Of Cloud Computing Infrastructures And Services,” http://www.cloudbus.org/cloudsim/, 2013, Último acesso: 29-junho-2013.
Amazon, “Amazon EC2 Instance Types,” http://aws.amazon.com/ec2/#instance, 2013, Último acesso: 29-junho-2013.
K. Park and V. S. Pai, “CoMon: a mostly-scalable monitoring system for PlanetLab,” SIGOPS Oper. Syst. Rev., vol. 40, no. 1, pp. 65–74, 2006.
PlanetLab, “An open platform for developing, deploying, and accessing planetary-scale services,” http://www.planet-lab.org, 2013, Último acesso: 29-junho-2013.
Publicado
23/10/2013
Como Citar
CASTRO, Pedro H. P.; CORRÊA, Sand; CARDOSO, Kleber V..
Uma Abordagem Baseada no Consumo de CPU e RAM para a Eficiência Energética em Centros de Dados para Computação em Nuvem. In: SIMPÓSIO EM SISTEMAS COMPUTACIONAIS DE ALTO DESEMPENHO (SSCAD), 14. , 2013, Porto de Galinhas.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2013
.
p. 118-125.
DOI: https://doi.org/10.5753/wscad.2013.16781.
