Vectorized Sequence-Based Chunking for Data Deduplication

Abstract

Data deduplication has gained wide acclaim as a mechanism to improve storage efficiency and conserve network bandwidth. Its most critical phase, data chunking, is responsible for the overall space savings achieved via the deduplication process. However, modern data chunking algorithms are slow and compute-intensive because they scan large amounts of data while simultaneously making data-driven boundary decisions. We present SeqCDC, a novel chunking algorithm that leverages lightweight boundary detection, content-defined skipping, and SSE/AVX acceleration to improve chunking throughput for large chunk sizes. Our evaluation shows that SeqCDC achieves 15x higher throughput than unaccelerated and 1.2x-1.35x higher throughput than vector-accelerated data chunking algorithms while minimally affecting deduplication space savings.