Uma Avaliação de Desempenho de Contêineres Docker Executando Diferentes SGBDs Relacionais

• Brena Santos UFPI
• Patricia Takako Endo UPE
• Francisco Airton Silva UFPI

DOI: https://doi.org/10.5753/wperformance.2019.6467

Abstract

The concept of computational virtualization, though old, has become a commonplace in enabling applications that share computing resources in the cloud infrastructure. Virtual machines (VMs) have brought benefits such as the ability to perform live migration to provide fault tolerance and load balancing. However, despite the advantages, VMs add extra layers of abstraction, resulting in loss of efficiency. As an alternative to VMs, today’s containers present them- selves as a lighter, better-performing solution. This work performs a performance evaluation based on sensitivity analysis with the objective of identifying the factors that most impact on the efficiency of a Database Management System (DBMS) using Docker as a container.

References

Amento, B., Hall, R. J., Joshi, K., and Purdy, K. H. (2018). Systems and methods for allocating and managing resources in an internet of things environment using location based focus of attention. US Patent App. 15/432,042.

Antony, J. (2006). Taguchi or classical design of experiments: a perspective from a practitioner. Sensor Review, 26(3):227–230.

Barik, R. K., Lenka, R. K., Rao, K. R., and Ghose, D. (2016). Performance analysis of virtual machines and containers in cloud computing. In Computing, Communication and Automation (ICCCA), 2016 International Conference on, pages 1204–1210. IEEE.

Dua, R., Raja, A. R., and Kakadia, D. (2014). Virtualization vs containerization to support paas. In Cloud Engineering (IC2E), 2014 IEEE International Conference on, pages 610–614. IEEE.

Fink, J. (2014). Docker: a software as a service, operating system-level virtualization framework. Code4Lib Journal, 25:29.

Gaur, N., Joshi, P., and Srivastava, R. (2017). Modelling database server sizing for con- current users using coloured petri-nets. In 2017 2nd International Conference on Com- munication Systems, Computing and IT Applications (CSCITA), pages 90–94. IEEE.

Gillan, C. J., Novakovic, A., Marshall, A. H., Shyamsundar, M., and Nikolopoulos, D. S. (2018). Expediting assessments of database performance for streams of respiratory parameters. Computers in biology and medicine, 100:186–195.

Gunst, R. F. (1996). Response surface methodology: process and product optimization using designed experiments.

Gupta, P., Agrawal, D., Chhabra, J., and Dhir, P. K. (2016). Iot based smart healthcare kit. In 2016 International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT), pages 237–242. IEEE.

Hsieh, Y.-C., Hong, H.-J., Tsai, P.-H., Wang, Y.-R., Zhu, Q., Uddin, M. Y. S., Venkatasu- bramanian, N., and Hsu, C.-H. (2018). Managed edge computing on internet-of-things devices for smart city applications. In NOMS 2018-2018 IEEE/IFIP Network Opera- tions and Management Symposium, pages 1–2. IEEE.

Jiang, L., Da Xu, L., Cai, H., Jiang, Z., Bu, F., and Xu, B. (2014). An iot-oriented data storage framework in cloud computing platform. IEEE Transactions on Industrial Informatics, 10(2):1443–1451.

Kuhn, D. R. and Reilly, M. J. (2002). An investigation of the applicability of design of ex- periments to software testing. In Software Engineering Workshop, 2002. Proceedings. 27th Annual NASA Goddard/IEEE, pages 91–95. IEEE.

Lazic, Z. R. (2006). Design of experiments in chemical engineering: a practical guide. John Wiley & Sons.

Miller, C., Thomas, D., Irigoyen, S. D., Hersberger, C., Nagy, Z., Rossi, D., and Schlu- eter, A. (2014). Bim-extracted energyplus model calibration for retrofit analysis of a historically listed building in switzerland. Proceedings ofSimBuild, 2014.

Morabito, R. (2016). A performance evaluation of container technologies on internet of things devices. In Computer Communications Workshops (INFOCOM WKSHPS), 2016 IEEE Conference on, pages 999–1000. IEEE.

Morabito, R. (2017). Virtualization on internet of things edge devices with container technologies: a performance evaluation. IEEE Access, 5:8835–8850.

Niu, M., Cheng, B., Zhai, Z., Feng, Y., and Chen, J. (2017). Poster: Docker-based self- organizing iot services architecture for smarthome. In Proceedings ofthe 15th Annual International Conference on Mobile Systems, Applications, and Services, pages 153– 153. ACM.

Ruiz Espejo, M. (2006). Design of experiments for engineers and scientists.

Seltman, H. J. (2012). Experimental design and analysis. Online at: http://www. stat. cmu. edu/, hseltman/309/Book/Book. pdf.

Shirinbab, S., Lundberg, L., and Casalicchio, E. (2017). Performance evaluation of con- tainer and virtual machine running cassandra workload. In Cloud Computing Tech- nologies and Applications (CloudTech), 2017 3rd International Conference of, pages 1–8. IEEE.

Siebertz, K., Van Bebber, D., and Hochkirchen, T. (2017). Statistische Versuchsplanung: design ofexperiments (DoE). Springer-Verlag.

Thomas, D., Miller, C., K¨ampf, J., and Schlueter, A. (2014). Multiscale co-simulation of energyplus and citysim models derived from a building information model. In Bausim 2014: Fifth German-Austrian IBPSA Conference.

Velasquez, W., Munoz-Arcentales, A., and Rodriguez, J. S. (2018). A case study: In- gestion analysis of wsn data in databases using docker. In 2018 1st International Conference on Computer Applications & Information Security (ICCAIS), pages 1–6. IEEE.

Xavier, M. G., Israel C. De Oliveira, F. D. R., Robson D. Dos Passos, K. J. M., and Rose, C. A. F. D. (2016). Containers or hypervisors, which is better for database consolidation?