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Evaluating Database Replication Mechanisms for Disaster Recovery in Cloud Environments

Relational databases are the most popular database system worldwide. The occurrence of failures in these systems may produce severe consequences for the business, such as data loss, customer dissatisfaction, and subsequent revenue loss. Consequently, many organizations have adopted disaster recovery (DR) solutions as an attempt to prevent data loss and ensure business continuity. Data replication for databases is one of the most used DR solution employed to guarantee data safety and availability. However, the analysis regarding DR aspects has been less explored. Therefore, in this paper, we present an integrated model-experiment approach to evaluate replication mechanisms in relational databases for DR purposes. We performed experiments in a geo-distributed cloud environment and developed analytic models to evaluate DR key-metrics such as availability, downtime, Recovery Time Objective (RTO), and Recovery Point Objective (RPO). The results revealed that the adoption of replication mechanisms could increase the system’s availability significantly. It also revealed that the replication mechanisms can guarantee RPO and RTO within seconds.