A Recommender for Resource Allocation in Compute Clouds Using Genetic Algorithms and SVR
Resumo
Alocação de recursos em Cloud Computing tem sido feito de forma reativa, dificultando garantias de serviço e gerando carga desnecessária de recursos ociosos. Para mitigar esses problemas, este trabalho propõe e avalia uma abordagem de alocação de recursos preditiva, implementado como um recomendador de configuração, com base em Support Vector Regression (SVR) e Algoritmos Genéticos (AG). Esta combinação é utilizada para estimar tempo de execução do aplicativo e recomenda uma configuração viável e válida de recursos na nuvem, sobre o tempo de execução e custos monetários. Como estudo de caso, as aplicações de aprendizagem de máquina com base na ferramenta Weka são escolhidos. Os resultados mostram que os tempos previstos foram muito perto dos reais, conseguindo uma estimativa eficiente de tempo e custo e sua consequente redução.
Palavras-chave:
Redes de Computadores, Plataforma de Virtualização, Serviços Web, Algoritmos Genéticos, Modelos Preditivos
Referências
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M. Patel, S. Chaudhary, and S. Garg, “Machine learning based statistical prediction model for improving performance of live virtual machine migration,” Journal of Engineering, vol. 2016, 2016.
K. Sembiring and A. Beyer, “Dynamic resource allocation for cloud-based media processing,” in Proceeding of the 23rd ACM Workshop on Network and Operating Systems Support for Digital Audio and Video. ACM, 2013, pp. 49–54.
J. J. Prevost, K. Nagothu, B. Kelley, and M. Jamshidi, “Prediction of cloud data center networks loads using stochastic and neural models,” in System of Systems Engineering (SoSE), 2011 6th International Conference on. IEEE, 2011, pp. 276–281.
O. Niehorster, A. Krieger, J. Simon, and A. Brinkmann, “Autonomic resource management with support vector machines,” in Proceedings of the 2011 IEEE/ACM 12th International Conference on Grid Computing. IEEE Computer Society, 2011, pp. 157–164.
A. A. Bankole and S. A. Ajila, “Predicting cloud resource provisioning using machine learning techniques,” in Electrical and Computer Engineering (CCECE), 2013 26th Annual IEEE Canadian Conference on. IEEE, 2013, pp. 1–4.
E. Hormozi, H. Hormozi, M. K. Akbari, and M. S. Javan, “Using of machine learning into cloud environment (a survey): managing and scheduling of resources in cloud systems,” in P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), 2012 Seventh International Conference on. IEEE, 2012, pp. 363–368.
Weka. (2017) The university of waikato. Weka 3: Data mining software in java. Disponível em: http://www.cs.waikato.ac.nz/ml/weka. Acesso em: Janeiro 2017.
HP. (2016) Hp eucalyptus. Disponível em: http://hphelion.com. Acesso em: Junho 2016.
Pentaho. (2017) Pentaho weka server datamining. Disponível em: http://wiki.pentaho.com/display/DATAMINING/Weka+Server. Acesso em: Janeiro 2017.
S. O. Rezende, Sistemas Inteligentes: Fundamentos e Aplicações. Manole, 2003.
P. J. Jonathan Kupferman, Jeff Silverman, “Scaling into the cloud,” ADVANCED OPERATING SYSTEMS, pp. 1–8, 2016.
C. R. Cunha, E. P. Morais, J. P. Sousa, and J. P. Gomes, “The role of cloud computing in the development of information systems for smes,” IBIMA Publishing, pp. 1–7, 2017. [Online]. Available: http://hdl.handle.net/10198/14061
S. M. Parikh, “A survey on cloud computing resource allocation techniques,” in Engineering (NUiCONE), 2013 Nirma University International Conference on. IEEE, 2013, pp. 1–5.
A. Baruchi, E. T. Midorikawa, and L. M. Sato, “Reducing virtual machine live migration overhead via workload analysis,” IEEE Latin America Transactions, vol. 13, no. 4, pp. 1178– 1186, 2015.
M. Patel, S. Chaudhary, and S. Garg, “Machine learning based statistical prediction model for improving performance of live virtual machine migration,” Journal of Engineering, vol. 2016, 2016.
K. Sembiring and A. Beyer, “Dynamic resource allocation for cloud-based media processing,” in Proceeding of the 23rd ACM Workshop on Network and Operating Systems Support for Digital Audio and Video. ACM, 2013, pp. 49–54.
J. J. Prevost, K. Nagothu, B. Kelley, and M. Jamshidi, “Prediction of cloud data center networks loads using stochastic and neural models,” in System of Systems Engineering (SoSE), 2011 6th International Conference on. IEEE, 2011, pp. 276–281.
O. Niehorster, A. Krieger, J. Simon, and A. Brinkmann, “Autonomic resource management with support vector machines,” in Proceedings of the 2011 IEEE/ACM 12th International Conference on Grid Computing. IEEE Computer Society, 2011, pp. 157–164.
A. A. Bankole and S. A. Ajila, “Predicting cloud resource provisioning using machine learning techniques,” in Electrical and Computer Engineering (CCECE), 2013 26th Annual IEEE Canadian Conference on. IEEE, 2013, pp. 1–4.
E. Hormozi, H. Hormozi, M. K. Akbari, and M. S. Javan, “Using of machine learning into cloud environment (a survey): managing and scheduling of resources in cloud systems,” in P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), 2012 Seventh International Conference on. IEEE, 2012, pp. 363–368.
Weka. (2017) The university of waikato. Weka 3: Data mining software in java. Disponível em: http://www.cs.waikato.ac.nz/ml/weka. Acesso em: Janeiro 2017.
HP. (2016) Hp eucalyptus. Disponível em: http://hphelion.com. Acesso em: Junho 2016.
Pentaho. (2017) Pentaho weka server datamining. Disponível em: http://wiki.pentaho.com/display/DATAMINING/Weka+Server. Acesso em: Janeiro 2017.
S. O. Rezende, Sistemas Inteligentes: Fundamentos e Aplicações. Manole, 2003.
P. J. Jonathan Kupferman, Jeff Silverman, “Scaling into the cloud,” ADVANCED OPERATING SYSTEMS, pp. 1–8, 2016.
C. R. Cunha, E. P. Morais, J. P. Sousa, and J. P. Gomes, “The role of cloud computing in the development of information systems for smes,” IBIMA Publishing, pp. 1–7, 2017. [Online]. Available: http://hdl.handle.net/10198/14061
S. M. Parikh, “A survey on cloud computing resource allocation techniques,” in Engineering (NUiCONE), 2013 Nirma University International Conference on. IEEE, 2013, pp. 1–5.
Publicado
27/11/2019
Como Citar
DOS REIS, Thiago Nelson Faria; TEIXEIRA, Mário Antonio Meireles ; DE ALMEIDA, João Dallyson Sousa ; DE PAIVA, Anselmo Cardoso.
A Recommender for Resource Allocation in Compute Clouds Using Genetic Algorithms and SVR. In: CONGRESSO LATINO-AMERICANO DE SOFTWARE LIVRE E TECNOLOGIAS ABERTAS (LATINOWARE), 16. , 2019, Foz do Iguaçu.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 76-83.
DOI: https://doi.org/10.5753/latinoware.2019.10336.
