An Advance Resource Reservation Approach in a Cloud Database Environment
Resumo
AA well-known challenge with long running time queries in database environments is how much time a query will take to execute. This prediction is relevant for several reasons. For instance, by knowing that a query will take longer to execute than desired, one resource reservation mechanism can be performed, which means reserving more resources in order to execute this query in a shorter time in a future request. In this research work, it is presented a proposal in which the use of an advance reservation mechanism in a cloud database environment, considering machine learning techniques, provides resource recommendation. The proposed model is presented, in addition to some experiments that evaluate benefits and the efficiency of this enhanced proposal.
Referências
Ahmad, M., Duan, S., Aboulnaga, A., and Babu, S. (2011). Predicting completion times of batch query workloads using interaction-aware models and simulation. In Proc. of the 14th Int. Conf. on Extending Database Technology, pages 449–460. ACM.
Assunção, M. D., Calheiros, R. N., Bianchi, S., Netto, M. A., and Buyya, R. (2015). Big data computing and clouds: Trends and future directions. Journal of Parallel and Distributed Computing, 79:3–15.
Bernstein, D. (2014). Containers and cloud: From lxc to docker to kubernetes. IEEE Cloud Computing, 1(3):81–84.
Duggan, J., Papaemmanouil, O., et al. (2014). Contender: A resource modeling approach for concurrent query performance prediction. In EDBT, pages 109–120.
Farias, V. A., Sousa, F. R., Maia, J. G., Gomes, J. P., and Machado, J. C. (2016). Machine learning approach for cloud nosql databases performance modeling. In Cluster, Cloud and Grid Computing, 2016 16th IEEE/ACM Int. Symposium on, pages 617–620. IEEE.
Funke, D., Brosig, F., and Faber, M. (2012). Towards truthful resource reservation in cloud computing. In Performance Evaluation Methodologies and Tools (VALUETOOLS), 2012 6th International Conference on, pages 253–262.
Ganapathi, A., Kuno, H., Dayal, U., Wiener, J. L., Fox, A., Jordan, M., and Patterson, D. (2009). Predicting multiple metrics for queries: Better decisions enabled by machine learning. In Data Engineering, 2009. IEEE 25th Int. Conf. on, pages 592–603. IEEE.
Gomes, E. and Dantas, M. A. R. (2014). An advance reservation mechanism to enhance throughput in an opportunistic high performance computing environment. In Network Computing and Applications, IEEE 13th Int. Symposium on, pages 221–228. IEEE.
Gupta, C., Mehta, A., and Dayal, U. (2008). Pqr: Predicting query execution times In Autonomic Computing, 2008. ICAC'08. for autonomous workload management. International Conference on, pages 13–22. IEEE.
Hasan, R. and Gandon, F. (2014). A machine learning approach to sparql query performance prediction. In Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2014 IEEE/WIC/ACM Int. Joint Conferences on, volume 1, pages 266–273. IEEE.
He, H. (2015). Virtual resource provision based on elastic reservation in cloud computing. Int. J. Netw. Virtual Organ., 15(1):30–47.
Inacio, E. C., Barbetta, P. A., and Dantas, M. A. (2017). A statistical analysis of the performance variability of read/write operations on parallel le systems. Procedia Computer Science, 108:2393–2397.
Kutner, M. H. et al. (2004). Applied linear regression models. McGraw-Hill/Irwin.
Lee, K., König, A. C., Narasayya, V., Ding, B., et al. (2016). Operator and query progress In Proceedings of the 2016 estimation in microsoft sql server live query statistics. International Conference on Management of Data, pages 1753–1764. ACM.
MacLaren, J. (2003). Advance reservations: State of the art. Global Grid Forum.
Matsunaga, A. and Fortes, J. A. (2010). On the use of machine learning to predict the time and resources consumed by applications. In Proc. of the 10th IEEE/ACM Int. Conf. on Cluster, Cloud and Grid Computing, pages 495–504. IEEE Computer Society.
Merkel, D. (2014). Docker: lightweight linux containers for consistent development and deployment. Linux Journal, 2014(239):2.
Nambiar, R. O. and Poess, M. (2006). The making of tpc-ds. In Proc. of the 32nd Int. Conf. on Very large data bases, pages 1049–1058. VLDB Endowment.
Pal, S. K. and Mitra, S. (1992). Multilayer perceptron, fuzzy sets, and classication. IEEE Transactions on neural networks, 3(5):683–697.
Quinlan, J. R. et al. (1992). Learning with continuous classes. In 5th Australian joint conference on articial intelligence, volume 92, pages 343–348. Singapore.
Singhal, R. and Nambiar, M. (2016). Predicting sql query execution time for large data volume. In Proceedings of the 20th International Database Engineering & Applications Symposium, pages 378–385. ACM.
Sulistio, A. and Buyya, R. (2004). A grid simulation infrastructure supporting advance reservation. In 16th International Conference on Parallel and Distributed Computing and Systems (PDCS 2004), pages 9–11.
Wang, C., Ma, W., Qin, T., Chen, X., Hu, X., and Liu, T.-Y. (2015). Selling reserved instances in cloud computing. In IJCAI, pages 224–231.
Wu, W., Chi, Y., Hacígümüs¸, H., and Naughton, J. F. (2013a). Towards predicting query execution time for concurrent and dynamic database workloads. Proceedings of the VLDB Endowment, 6(10):925–936.
Wu, W., Chi, Y., Zhu, S., Tatemura, J., Hacigümüs, H., and Naughton, J. F. (2013b). Predicting query execution time: Are optimizer cost models really unusable? In Data Engineering (ICDE), 2013 IEEE 29th Int. Conf. on, pages 1081–1092. IEEE.
Assunção, M. D., Calheiros, R. N., Bianchi, S., Netto, M. A., and Buyya, R. (2015). Big data computing and clouds: Trends and future directions. Journal of Parallel and Distributed Computing, 79:3–15.
Bernstein, D. (2014). Containers and cloud: From lxc to docker to kubernetes. IEEE Cloud Computing, 1(3):81–84.
Duggan, J., Papaemmanouil, O., et al. (2014). Contender: A resource modeling approach for concurrent query performance prediction. In EDBT, pages 109–120.
Farias, V. A., Sousa, F. R., Maia, J. G., Gomes, J. P., and Machado, J. C. (2016). Machine learning approach for cloud nosql databases performance modeling. In Cluster, Cloud and Grid Computing, 2016 16th IEEE/ACM Int. Symposium on, pages 617–620. IEEE.
Funke, D., Brosig, F., and Faber, M. (2012). Towards truthful resource reservation in cloud computing. In Performance Evaluation Methodologies and Tools (VALUETOOLS), 2012 6th International Conference on, pages 253–262.
Ganapathi, A., Kuno, H., Dayal, U., Wiener, J. L., Fox, A., Jordan, M., and Patterson, D. (2009). Predicting multiple metrics for queries: Better decisions enabled by machine learning. In Data Engineering, 2009. IEEE 25th Int. Conf. on, pages 592–603. IEEE.
Gomes, E. and Dantas, M. A. R. (2014). An advance reservation mechanism to enhance throughput in an opportunistic high performance computing environment. In Network Computing and Applications, IEEE 13th Int. Symposium on, pages 221–228. IEEE.
Gupta, C., Mehta, A., and Dayal, U. (2008). Pqr: Predicting query execution times In Autonomic Computing, 2008. ICAC'08. for autonomous workload management. International Conference on, pages 13–22. IEEE.
Hasan, R. and Gandon, F. (2014). A machine learning approach to sparql query performance prediction. In Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2014 IEEE/WIC/ACM Int. Joint Conferences on, volume 1, pages 266–273. IEEE.
He, H. (2015). Virtual resource provision based on elastic reservation in cloud computing. Int. J. Netw. Virtual Organ., 15(1):30–47.
Inacio, E. C., Barbetta, P. A., and Dantas, M. A. (2017). A statistical analysis of the performance variability of read/write operations on parallel le systems. Procedia Computer Science, 108:2393–2397.
Kutner, M. H. et al. (2004). Applied linear regression models. McGraw-Hill/Irwin.
Lee, K., König, A. C., Narasayya, V., Ding, B., et al. (2016). Operator and query progress In Proceedings of the 2016 estimation in microsoft sql server live query statistics. International Conference on Management of Data, pages 1753–1764. ACM.
MacLaren, J. (2003). Advance reservations: State of the art. Global Grid Forum.
Matsunaga, A. and Fortes, J. A. (2010). On the use of machine learning to predict the time and resources consumed by applications. In Proc. of the 10th IEEE/ACM Int. Conf. on Cluster, Cloud and Grid Computing, pages 495–504. IEEE Computer Society.
Merkel, D. (2014). Docker: lightweight linux containers for consistent development and deployment. Linux Journal, 2014(239):2.
Nambiar, R. O. and Poess, M. (2006). The making of tpc-ds. In Proc. of the 32nd Int. Conf. on Very large data bases, pages 1049–1058. VLDB Endowment.
Pal, S. K. and Mitra, S. (1992). Multilayer perceptron, fuzzy sets, and classication. IEEE Transactions on neural networks, 3(5):683–697.
Quinlan, J. R. et al. (1992). Learning with continuous classes. In 5th Australian joint conference on articial intelligence, volume 92, pages 343–348. Singapore.
Singhal, R. and Nambiar, M. (2016). Predicting sql query execution time for large data volume. In Proceedings of the 20th International Database Engineering & Applications Symposium, pages 378–385. ACM.
Sulistio, A. and Buyya, R. (2004). A grid simulation infrastructure supporting advance reservation. In 16th International Conference on Parallel and Distributed Computing and Systems (PDCS 2004), pages 9–11.
Wang, C., Ma, W., Qin, T., Chen, X., Hu, X., and Liu, T.-Y. (2015). Selling reserved instances in cloud computing. In IJCAI, pages 224–231.
Wu, W., Chi, Y., Hacígümüs¸, H., and Naughton, J. F. (2013a). Towards predicting query execution time for concurrent and dynamic database workloads. Proceedings of the VLDB Endowment, 6(10):925–936.
Wu, W., Chi, Y., Zhu, S., Tatemura, J., Hacigümüs, H., and Naughton, J. F. (2013b). Predicting query execution time: Are optimizer cost models really unusable? In Data Engineering (ICDE), 2013 IEEE 29th Int. Conf. on, pages 1081–1092. IEEE.
Publicado
17/10/2017
Como Citar
DA S. SEGALIN, Vinicius; F. DORNELES, Carina; A. R. DANTAS, Mario.
An Advance Resource Reservation Approach in a Cloud Database Environment. In: SIMPÓSIO EM SISTEMAS COMPUTACIONAIS DE ALTO DESEMPENHO (SSCAD), 18. , 2017, Campinas.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2017
.
p. 208-219.
DOI: https://doi.org/10.5753/wscad.2017.259.
