Periodic and random incremental backup policies in reliability theory

For a 24/7 database system, backups should be implemented right after a large volume of data has been updated, putting their backup windows in non-busy states with user’s convenience.From this viewpoint, this paper studies periodic and random incremental backup policies, in which, incremental backup is implemented right after data update and full backup is performed at periodic times \(\varvec{KT}\) $\mathit{KT}$ , or at a number \(\varvec{N}\) $\mathit{N}$ of data updates, respectively. We firstly describe the stochastic processes of data update and database failure, and then model the expected cost rates for data backup and data restoration.Respective \(\varvec{K^*, N^*, K_f^*}\) ${\mathit{K}}^{\mathbf{\ast }}\mathbf{,}{\mathit{N}}^{\mathbf{\ast }}\mathbf{,}{\mathit{K}}_{\mathit{f}}^{\mathbf{\ast }}$ , and \(\varvec{N_f^*}\) ${\mathit{N}}_{\mathit{f}}^{\mathbf{\ast }}$ are obtained to minimize their expected cost rates in analytical ways, respectively. Finally, numerical examples are given to illustrate the optimum policies.