One Size Does Not Fit All: The Case for Chunking Configuration in Backup Deduplication

Abstract

Data backup is regularly required by both enterprise and individual users to protect their data from unexpected loss. There are also various commercial data deduplication systems or software that help users to eliminate duplicates in their backup data to save storage space. In data deduplication systems, the data chunking process splits data into small chunks. Duplicate data is identified by comparing the fingerprints of the chunks. The chunk size setting has significant impact on deduplication performance. A variety of chunking algorithms have been proposed in recent studies. In practice, existing systems often set the chunking configuration in an empirical manner. A chunk size of 4KB or 8KB is regarded as the sweet spot for good deduplication performance. However, the data storage and access patterns of users vary and change along time, as a result, the empirical chunk size setting may not lead to a good deduplication ratio and sometimes results in difficulties of storage capacity planning. Moreover, it is difficult to make changes to the chunking settings once they are put into use as duplicates in data with different chunk size settings cannot be eliminated directly. In this paper, we propose a sampling-based chunking method and develop a tool named SmartChunker to estimate the optimal chunking configuration for deduplication systems. Our evaluations on real-world datasets demonstrate the efficacy and efficiency of SmartChunker.