Authors: Júlio Mendonça; Ricardo Lima; Ermeson Andrade
Addresses: Informatics Center, Federal University of Pernambuco, Recife, Brazil ' Informatics Center, Federal University of Pernambuco, Recife, Brazil ' Department of Computing, Federal Rural University of Pernambuco, Recife, Brazil
Abstract: Systems outages can have disastrous effects on businesses such as data loss, customer dissatisfaction, and subsequent revenue loss. Disaster recovery (DR) solutions have been adopted by companies to minimise the effects of these outages. However, the selection of an optimal DR solution is difficult since there does not exist a single solution that suits the requirement of every company (e.g., availability and costs). In this paper, we propose an integrated model-experiment approach to evaluate DR solutions. We perform experiments in different real-world DR solutions and propose analytic models to evaluate these solutions regarding DR key-metrics: steady-state availability, recovery time objective (RTO), recovery point objective (RPO), downtime, and costs. The results reveal that DR solutions can significantly improve availability and minimise costs. Also, a sensitivity analysis identifies the parameters that most affect the RPO and RTO of the DR adopted solutions.
Keywords: backup-as-a-service; cloud computing; disaster recovery; disaster tolerance; fault-tolerance; Petri nets; stochastic modelling.
International Journal of Grid and Utility Computing, 2020 Vol.11 No.5, pp.683 - 704
Received: 03 May 2019
Accepted: 12 Aug 2019
Published online: 01 Aug 2020 *