Improving Restore Performance in Deduplication Systems via a Cost-Efficient Rewriting Scheme

Abstract

In chunk-based deduplication systems, logically consecutive chunks are physically scattered in different containers after deduplication, which results in the serious fragmentation problem. The fragmentation significantly reduces the restore performance due to reading the scattered chunks from different containers. Existing work aims to rewrite the fragmented duplicate chunks into new containers to improve the restore performance, which however produces the redundancy among containers, decreasing the deduplication ratio and resulting in redundant chunks in containers retrieved to restore the backup, which wastes limited disk bandwidth and decreases restore speed. To improve the restore performance while ensuring the high deduplication ratio, this paper proposes a cost-efficient submodular maximization rewriting scheme (SMR). SMR first formulates the defragmentation as an optimization problem of selecting suitable containers, and then builds a submodular maximization model to address this problem by selecting containers with more distinct referenced chunks. Moveover, this paper further leverages the grouped form, i.e., GSMR, to reduce the fragmented chunks caused by the accumulated differences among backup versions. We implement SMR in the deduplication system, which is evaluated via three real-world datasets. Experimental results demonstrate that SMR is superior to the state-of-the-art work in terms of the restore performance as well as deduplication ratio, and GSMR further improves the restore performance. We have released the source code of SMR in Github for public use.