Evaluation of a BackupasaService Environment for Disaster Recovery Systems unavailability may produce severe consequences for modern business such as data loss, customer dissatisfaction, and subsequent revenue loss. Disaster recovery (DR) solutions have been adopted by many organizations as an attempt to prevent data loss and ensure business continuity. With the cloud computing expansion, different cloud providers have been offering lowcost solutions for DR purposes such as the Backupasaservice (BaaS) for consumers. Therefore, in this paper, we present an integrated modelexperiment approach to evaluate a BaaS environment for DR purposes. We use analytic models and faultinjection experiments to evaluate DR keymetrics such as availability, downtime, Recovery Time Objective (RTO), and Recovery Point Objective (RPO) in a realworld BaaS environment. The results revealed that the environment availability can vary according to the amount of data to backed up and restored. Besides, a sensitivity analysis shows that the RTO and RPO are mainly influenced by the the mean time to recover from a disaster and the backup interval, respectively.

Exploring A Forecasting Structure for the Capacity Usage in Backup Storage Systems Protecting data from loss is important in many domains, particularly business. Businesses generally employ thirdparty protection services to routinely back up data to storage. As the scale of the backup system and the volume of backup data continue to grow significantly, backup jobs fail increasingly frequently. In this study, we present an analysis of data protection system reports written over two years and collected from 3,500 backup systems. We found that inadequate capacity is among the two most frequent causes of the failure of backups. Little research has targeted the development of a forecasting tool to predict the backup storage capacity to mitigate this failure, and accurate prediction becomes more challenging as the number of clients served by the storage increases. Our research highlights the characteristics of enterprise backup storage data and uses the information examined here to develop a forecasting structure. We tested our forecasting structure on empirically obtained data. In comparison with a prevalent backup system forecasting tool, our structure provides better and more robust predictions of the capacity of backup storage systems as well as the size of data generated by a given number of clients and the associated deduplication ratios.