Data backup policies with failure-oblivious computing in reliability theory

In-memory database systems are becoming an efficient technology to achieve the goal of high throughput rate and low latency time, however, they are more vulnerable than disk-based database systems due to the feature of their running carriers, so that it becomes a critical problem to design data backup and recovery policies to prevent memory failures for in-memory database management systems. From this viewpoint, this paper firstly describes the stochastic processes of bulk-data update, triggers of failure-oblivious computing and in-memory database failure, and then model the expected cost rates for data backup and recovery when full backups are implemented at time T and at bulk-data update N, respectively. In order to compare the policies of T and N, integrated models of the backup policies implemented at time T and at bulk-data update N are studied, using the triggering approaches of first and last in maintenance theory. Furthermore, the policies of T and N are reconsidered when full backup is planned at the completion of the forthcoming bulk-data update. In addition, a cumulative cost of failure-oblivious computing is considered in the modified backup policies. All of the expected cost rates and their optimum policies of full backups are obtained in analytical ways.