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Data reduction in storage systems is becoming increasingly important as aneffective solution to minimize the management cost of a data center. Tomaximize data-reduction efficiency, existing post-deduplicationdelta-compression techniques perform delta compression along with traditionaldata deduplication and lossless compression. Unfortunately, we observe thatexisting techniques achieve significantly lower data-reduction ratios than theoptimal due to their limited accuracy in identifying similar data blocks.  In this paper, we propose DeepSketch, a new reference search technique forpost-deduplication delta compression that leverages the learning-to-hash methodto achieve higher accuracy in reference search for delta compression, therebyimproving data-reduction efficiency. DeepSketch uses a deep neural network toextract a data block's sketch, i.e., to create an approximate data signature ofthe block that can preserve similarity with other blocks. Our evaluation usingeleven real-world workloads shows that DeepSketch improves the data-reductionratio by up to 33% (21% on average) over a state-of-the-art post-deduplicationdelta-compression technique.

DeepSketch: A New Machine Learning-Based Reference Search Technique for Post-Deduplication Delta Compression