Reducing the de-linearization of data placement to improve deduplication performance

Data deduplication is a lossless compression technology that replaces the redundant data chunks with pointers pointing to the already-stored ones. Due to this intrinsic data elimination feature, the deduplication commodity would delinearize the data placement and force the data chunks that belong to the same data object to be divided into multiple separate parts. In our preliminary study, it is found that the de-linearization of the data placement would weaken the data spatial locality that is used for improving data read performance, deduplication throughput and efficiency in some deduplication approaches, which significantly affects the deduplication performance. In this paper, we first analyze the negative effect of the de-linearization of data placement to the data deduplication performance with some examples and experimental evidences, and then propose an effective approach to reduce the de-linearization of data placement by sacrificing little compression ratios. The experimental evaluation driven by the real world datasets shows that our proposed approach effectively reduces the de-linearization of the data placement and enhances the data spatial locality, which significantly improves the deduplication performances including deduplication throughput, deduplication efficiency and data read performance, while at the cost of little compression ratios.