Improving Online Restore Performance of Backup Storage via Historical File Access Pattern

The performance of data restore is one of the key indicators of user experience for backup storage systems. Compared to the traditional offline restore process, online restore reduces downtime during backup restoration, allowing users to operate on already restored files while other files are still being restored. This approach improves availability during restoration tasks but suffers from a critical limitation: inconsistencies between the access sequence and the restore sequence. In many cases, the file a user needs to access at a given moment may not yet be restored, resulting in significant delays and poor user experience. To this end, we present Histore, which builds on the user’s historical access sequence to schedule the restore sequence, in order to reduce users’ access delayed time. Histore includes three restore approaches: (i) the frequency-based approach, which restores files based on historical file access frequencies and prioritizes ensuring the availability of frequently accessed files; (ii) the graph-based approach, which preferentially restores the frequently accessed files as well as their correlated files based on historical access patterns, and (iii) the trie-based approach, which restores particular files based on both users’ real-time and historical access patterns to deduce and restore the files to be accessed in the near future. We implement a prototype of Histore and evaluate its performance from multiple perspectives. Trace-driven experiments on two datasets show that Histore significantly reduces users’ delay time by 4-700 $\times$ with only 1.0%–14.5% additional performance overhead.