Experimental Evaluation of Database Replication for Disaster Recovery
Abstract
IT systems are essential for the operations of any modern business.Such systems must support operations of their corresponding company under any conditions. Disaster Recovery (DR) strategies have been implemented to help organizations mitigate unexpected failures and reduce unnecessary expenses. However, to the best of our knowledge, no other work experimentally analyzes data replication at the database layer with a focus on DR strategies. Therefore, this work evaluates a relational database replication as a mean of implementing a DR solution. We use a real testbed in a public cloud environment to perform extensive experiments aimed at implementing the replication provided by MySQL, considering various scenarios in the context of DR. Our results show how response time, Recovery Point Objective (RPO) and Recovery Time Objective (RTO) vary according to the size of the replicated data, the synchronization type (ex.: asynchronous or semi-synchronous) and the configuration of the slave servers. This work can assist DR coordinators or individuals to decide which database replication configuration for disaster recovery is best for their work environment.
References
Bauer, E., Adams, R., and Eustace, D. (2011).Beyond Redundancy: How Geographic Redundancy Can Improve Service Availability and Reliability of Computer-Based Systems. Wiley.
DB engines (2020). Db-engines ranking. [Online].https://bit.ly/2s90XvI.
Google (2020). Google Cloud. [Online].https://cloud.google.com.Halili,
E. H. (2008).Apache JMeter: A practical beginner’s guide to automated testing and performance measurement for your websites. Packt Publishing Ltd.
Hollander, M. and Wolfe, D. (1999).Nonparametric Statistical Methods. Wiley Series inProbability and Statistics. Wiley.
Jogi, V. D. and Sinha, A. (2016). Performance evaluation of MySQL, Cassandra and HBase for heavy write operation. In 2016 3rd International Conference on Recent Advances in Information Technology (RAIT), pages 586–590. IEEE.
Marsan, M., Balbo, G., Chiola, G., Conte, G., Donatelli, S., and Franceschinis, G.(1991). An introduction to generalized stochastic petri nets.Microelectronics Reliability, 31(4):699 – 725.
Mendonça, J., Medeiros, W., Andrade, E., Maciel, R., Maciel, P., and Lima, R. (2019).Evaluating database replication mechanisms for disaster recovery in cloud environ-ments. In 2019 IEEE International Conference on Systems, Man and Cybernetics(SMC), pages 2358–2363. IEEE.
Mendonça, J., Andrade, E., Endo, P. T., and Lima, R. (2019). Disaster recovery solutionsfor it systems: A systematic mapping study. Journal of Systems and Software, 149:511– 530.
Oracle (2020a). MySQL 8.0 Reference Manual. [Online].https://dev.mysql.com/doc/refman/8.0/en/.
Oracle (2020b). The BLOB and TEXT Types. [Online].https://dev.mysql.com/doc/refman/8.0/en/blob.html.
Reese, G. (2009).Cloud application architectures: building applications and infrastruc-ture in the cloud. ”O’Reilly Media, Inc.”.
Rodrigues, M., Vasconcelos, B., Gomes, C., and Tavares, E. (2019). Evaluation of nosql dbms in private cloud environment: An approach based on stochastic modeling. In 2019 IEEE International Systems Conference (SysCon), pages 1–7. IEEE.
Santana, M., Armendáriz-Iñigo, J. E., and Muñoz-Escoí, F. D. (2016). Evaluation of Database Replication Techniques for Cloud Systems. Computing and Informatics, 34(5):973–995.
Shay, T. (2018). Most popular databases in 2018 according to stackoverflow survey .[Online]. https://bit.ly/2DCwqhj.
Unitrends (2019). Data protection, cloud, and proof draas delivers – unitrends 2019 sur-vey results. Tech Report. [Online]. https://bit.ly/2XubXDo.
Wang, H., Li, J., Zhang, H., and Zhou, Y. (2014). Benchmarking replication and consistency strategies in cloud serving databases: Hbase and cassandra. In Workshop on BigData Benchmarks, Performance Optimization, and Emerging Hardware, pages 71–82.Springer.
Zetta, I. (2016). State of Disaster Recovery 2016. [Online].https://bit.ly/2H6TwhN.
