Exploiting SLAs through Application of Economic Analysis on Datacenters’ Autonomic Management
Resumo
Optimizing resource distribution in datacenters can achieve several economic benefits, including increasing energy efficiency, better workload performance, and higher levels of job acceptance. Although some studies propose strategies that increase resource utilization whilst meeting a certain QoS, there is a perspective yet to be exploited: the Service Level Agreements (SLA) amortization. We believe that keeping service levels far above the minimum agreed is a missed opportunity to apply more aggressive strategies to reduce allocated resource fragmentation and increase even further the benefits mentioned above. In this work, we propose a novel server rebalancing strategy based on economic systems, able to capitalize from the SLAs by keeping the harmony between the aggressive competition of Free Markets and the safe control of Regulated Markets. The proposed strategy is evaluated with a modified version of CloudSim Plus simulator. The outcomes show that the SLA exploitation strategy led to an economy of 8.6% up to 23% for different mechanism configuration.
Palavras-chave:
SLA exploitation, server rebalancing, resource fragmentation, energy saving
Referências
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Amazon Web Services (2019). Amazon EC2 Service Level Agreements. https://aws.amazon.com/ec2/sla/?nc1=h_ls.
Bittencourt, L. F., Goldman, A., Madeira, E. R., da Fonseca, N. L., and Sakellariou, R. (2018). Scheduling in distributed systems: A cloud computing perspective. Computer Science Review, 30:31 – 54.
Carvalho, M., Menascé, D. A., and Brasileiro, F. (2017). Capacity planning for IaaS cloud providers offering multiple service classes. Future Generation Computer Systems, 77:97 – 111.
Filho, M. C. S., Oliveira, R. L., Monteiro, C. C., Inácio, P. R. M., 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.
Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P. P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q. M., Tziritas, N., Vishnu, A., Khan, S. U., and Zomaya, A. Y. (2016). A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing, 98(7):751–774.
Helms, M. M. (2005). Encyclopedia of Management. GALE Group, 1st edition.
Huberman, B. A. and Hogg, T. (1995). Distributed computation as an economic system. volume 9, pages 141–152.
Ibidunmoye, O., Hernández-Rodriguez, F., and Elmroth, E. (2015). Performance anomaly detection and bottleneck identification. ACM Comput. Surv., 48(1):4:1–4:35.
Lee, Y.-C. and Zomaya, A. (2010). Energy efficient utilization of resources in cloud computing systems. 60:268–280.
Microsoft Azure (2019). SLA for Virtual Machines. https://azure.microsoft.com/en-us/support/legal/sla/virtual-machines/v1_8/.
Muller, N. J. (1999). Managing service level agreements. International Journal of Network Management, 9(3):155–166.
Shehabi, A., Smith, S., Horner, N., Azevedo, I., Brown, R., Koomey, J., Masanet, E., Sartor, D., Herrlin, M., and Lintner, W. (2016). United states data center energy usage report, berkeley, california. Technical report.
Silva, G., Lopes, R., Brasileiro, F., Carvalho, M., Morais, F., Mafra, J., and Turull, D. (2019). Escalonamento justo em infraestruturas de nuvem com múltiplas classes de servico. In Proc. of the 2019 Brazilian Symposium of Computer Networks and Distributed Systems.
Song, W., Xiao, Z., Chen, Q., and Luo, H. (2014). Adaptive resource provisioning for the cloud using online bin packing. IEEE Transactions on Computers, 63(11):2647–2660.
Svärd, P., Li, W., Wadbro, E., Tordsson, J., and Elmroth, E. (2015). Continuous datacenter consolidation. In IEEE 7th International Conference on Cloud Computing Technology and Science, pages 387–396.
U.S. Energy Information Administration (2019). Electric Power Monthly. https://www.eia.gov/electricity/monthly/epm_table_grapher.php?t=epmt_5_6_a. Online; accessed December 2019.
Usmani, Z. and Singh, S. (2016). A survey of virtual machine placement techniques in a cloud data center. Procedia Computer Science, 78:491 – 498. 1st Intern. Conference on Information Security I& Privacy 2015.
Verma, A., Pedrosa, L., Korupolu, M., Oppenheimer, D., Tune, E., and Wilkes, J. (2015). Large-scale cluster management at google with borg. In Proc. of the Tenth European Conference on Computer Systems, EuroSys ’15, pages 18:1–18:17, New York, NY, USA. ACM.
Amazon Web Services (2019). Amazon EC2 Service Level Agreements. https://aws.amazon.com/ec2/sla/?nc1=h_ls.
Bittencourt, L. F., Goldman, A., Madeira, E. R., da Fonseca, N. L., and Sakellariou, R. (2018). Scheduling in distributed systems: A cloud computing perspective. Computer Science Review, 30:31 – 54.
Carvalho, M., Menascé, D. A., and Brasileiro, F. (2017). Capacity planning for IaaS cloud providers offering multiple service classes. Future Generation Computer Systems, 77:97 – 111.
Filho, M. C. S., Oliveira, R. L., Monteiro, C. C., Inácio, P. R. M., 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.
Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P. P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q. M., Tziritas, N., Vishnu, A., Khan, S. U., and Zomaya, A. Y. (2016). A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing, 98(7):751–774.
Helms, M. M. (2005). Encyclopedia of Management. GALE Group, 1st edition.
Huberman, B. A. and Hogg, T. (1995). Distributed computation as an economic system. volume 9, pages 141–152.
Ibidunmoye, O., Hernández-Rodriguez, F., and Elmroth, E. (2015). Performance anomaly detection and bottleneck identification. ACM Comput. Surv., 48(1):4:1–4:35.
Lee, Y.-C. and Zomaya, A. (2010). Energy efficient utilization of resources in cloud computing systems. 60:268–280.
Microsoft Azure (2019). SLA for Virtual Machines. https://azure.microsoft.com/en-us/support/legal/sla/virtual-machines/v1_8/.
Muller, N. J. (1999). Managing service level agreements. International Journal of Network Management, 9(3):155–166.
Shehabi, A., Smith, S., Horner, N., Azevedo, I., Brown, R., Koomey, J., Masanet, E., Sartor, D., Herrlin, M., and Lintner, W. (2016). United states data center energy usage report, berkeley, california. Technical report.
Silva, G., Lopes, R., Brasileiro, F., Carvalho, M., Morais, F., Mafra, J., and Turull, D. (2019). Escalonamento justo em infraestruturas de nuvem com múltiplas classes de servico. In Proc. of the 2019 Brazilian Symposium of Computer Networks and Distributed Systems.
Song, W., Xiao, Z., Chen, Q., and Luo, H. (2014). Adaptive resource provisioning for the cloud using online bin packing. IEEE Transactions on Computers, 63(11):2647–2660.
Svärd, P., Li, W., Wadbro, E., Tordsson, J., and Elmroth, E. (2015). Continuous datacenter consolidation. In IEEE 7th International Conference on Cloud Computing Technology and Science, pages 387–396.
U.S. Energy Information Administration (2019). Electric Power Monthly. https://www.eia.gov/electricity/monthly/epm_table_grapher.php?t=epmt_5_6_a. Online; accessed December 2019.
Usmani, Z. and Singh, S. (2016). A survey of virtual machine placement techniques in a cloud data center. Procedia Computer Science, 78:491 – 498. 1st Intern. Conference on Information Security I& Privacy 2015.
Verma, A., Pedrosa, L., Korupolu, M., Oppenheimer, D., Tune, E., and Wilkes, J. (2015). Large-scale cluster management at google with borg. In Proc. of the Tenth European Conference on Computer Systems, EuroSys ’15, pages 18:1–18:17, New York, NY, USA. ACM.
Publicado
07/12/2020
Como Citar
FALCÃO, Eduardo Lucena; CAVALCANTE, Lucas Medeiros; FALCÃO, Rafael Vieira; NUNES, José Benardi Souza; OLIVEIRA, Kaio Kassiano Moura; BRITO, Andrey Monteiro.
Exploiting SLAs through Application of Economic Analysis on Datacenters’ Autonomic Management. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 38. , 2020, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 281-294.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2020.12289.
