Ashwin Narasimha

Hardware accelerator for similarity based data dedupe

Data deduplication has proven important in backup storage systems as large amount of identical or similar data chunks exist. Recent studies have shown the great potential of data deduplication in primary storage and storage caches. Deduplications in these environments require high speed processing not to drag down production performance. This paper presents a hardware accelerator for similarity based data deduplication. It implements three compute-intensive kernel modules to improve throughput and latency in dedupe systems: sketch computation for data blocks, index searching for reference block, and delta encoding over similar blocks. Adopting pipelined computation and parallel data lookup across multiple hardware modules, our HW design is capable of processing high throughput data traffic by working on multiple data units concurrently, thus enabling wire speed dedupe for data stream where similar blocks present. Using a PC host system connected to the FPGA-based accelerator through a PCIe Gen 2×4 interface, our experiments show that the similarity based data dedupe performs 30% better in data reduction ratio than conventional dedupe techniques that look at identical blocks only. By comparing the hardware implementation with its software counterpart, the experimental results show that our preliminary FPGA implementation with maximum clock speed of 250MHz achieves at least 6 times improvement in latency over the software implementation running on state-of-art servers.