Prefetch-aware fingerprint cache management for data deduplication systems

Abstract

Data deduplication has been widely utilized in large-scale storage systems, particularly backup systems. Data deduplication systems typically divide data streams into chunks and identify redundant chunks by comparing chunk fingerprints. Maintaining all fingerprints in memory is not cost-effective because fingerprint indexes are typically very large. Many data deduplication systems maintain a fingerprint cache in memory and exploit fingerprint prefetching to accelerate the deduplication process. Although fingerprint prefetching can improve the performance of data deduplication systems by leveraging the locality of workloads, inaccurately prefetched fingerprints may pollute the cache by evicting useful fingerprints. We observed that most of the prefetched fingerprints in a wide variety of applications are never used or used only once, which severely limits the performance of data deduplication systems. We introduce a prefetch-aware fingerprint cache management scheme for data deduplication systems (PreCache) to alleviate prefetch-related cache pollution. We propose three prefetch-aware fingerprint cache replacement policies (PreCache-UNU, PreCache-UOO, and PreCache-MIX) to handle different types of cache pollution. Additionally, we propose an adaptive policy selector to select suitable policies for prefetch requests. We implement PreCache on two representative data deduplication systems (Block Locality Caching and SiLo) and evaluate its performance utilizing three real-world workloads (Kernel, MacOS, and Homes). The experimental results reveal that PreCache improves deduplication throughput by up to 32.22% based on a reduction of on-disk fingerprint index lookups and improvement of the deduplication ratio by mitigating prefetch-related fingerprint cache pollution.