Deduplication results inevitably in data fragmentation, because logically continuous data is scattered across many disk locations. In this work we focus on fragmentation caused by duplicates from previous backups of the same backup set, since such duplicates are very common due to repeated full backups containing a lot of unchanged data. For systems with in-line dedup which detects duplicates during writing and avoids storing them, such fragmentation causes data from the latest backup being scattered across older backups. As a result, the time of restore from the latest backup can be significantly increased, sometimes more than doubled.

Data deduplication, implemented usually with content defined chunking (CDC), is today one of key features of advanced storage systems providing space for backup applications. Although simple and effective, CDC generates chunks with sizes clustered around expected chunk size, which is globally fixed for a given storage system and applies to all backups. This creates opportunity for improvement, as the optimal chunk size for deduplication varies not only among backup datasets, but also within one dataset: long stretches of unchanged data favor larger chunks, whereas regions of change prefer smaller ones.