Availability Analysis of a Disaster Recovery Solution Through Stochastic Models and Fault Injection Experiments

The Information Technology (IT) systems of most organizations must support their operations 24 hours a day, 7 days a week. Systems unavailability may have serious consequences such as data loss, customer dissatisfaction, and subsequent revenue loss. With the popularity of cloud computing, the adoption of cloud-based disaster recovery (DR) solutions has gained more space to prevent data loss and ensure business continuity. However, disaster recovery solutions are not cheap and do not exist as a single solution that suits every requirement (e.g., availability and costs). In this paper, we present an integrated model-experiment approach to evaluate cloud-based disaster recovery solutions. We use Stochastic Petri Nets (SPNs) and fault-injection experiments to evaluate availability related metrics like steady-state availability and downtime. To demonstrate the feasibility of our approach, distinct real-world cloud-based DR solutions (e.g., active/active and active/standby) are modeled and analyzed. The results revealed that disaster recovery solution significantly improves system availability and minimizes the downtime costs. In addition, our numerical analysis shows the statistical correspondence between the results of the experiments and models.