一句话：提出层次化超特征与假阳性过滤，在去重后差分压缩中更高效地发现相似块，提升备份压缩率。
去重通过识别并删除重复块来压缩备份数据，但无法发现仅相似的块，因而限制了进一步的数据缩减。Palantir 针对去重后的差分压缩，提出层次化超特征：不同层次对块相似性具有不同敏感度，可召回更多相似块候选。为避免低质量候选损害压缩，Palantir 引入假阳性过滤器，丢弃对整体数据缩减无益的匹配块。存储额外超特征的开销通过利用备份流的时间局部性加以缓解。实验表明，相比 N-Transform 和 Odess，Palantir 将包含去重的整体备份压缩率提升 7.3%，相比 Finesse 提升 26.5%，而吞吐开销控制在 7.7% 以内。
Deduplication compresses backup data by identifying and removing duplicate blocks. However, deduplication cannot detect when two blocks are very similar, which opens up opportunities for further data reduction using delta compression. Most existing works find similar blocks by characterizing each block by a set of features and matching similar blocks using coarse-grained super-features. If two blocks share a super-feature, delta compression only needs to store their delta for the new block. Existing delta compression techniques constrain their super-features to find only matching blocks that are likely to be very similar. Palantir introduces hierarchical super-features with different sensitivities to block similarities to find more candidates of similar blocks and increase overall backup compression including deduplication by 7.3% over N-Transform and Odess and 26.5% over Finesse. The overhead of Palantir for storing additional super-features is overcome by exploiting temporal localities of backup streams, and the throughput penalty is within 7.7%. Palantir also introduces a false positive filter to discard matching blocks that are counterproductive for the overall data reduction.
@inproceedings{HuangWSXXB24,
title = {Palantir: Hierarchical Similarity Detection for Post-Deduplication Delta Compression},
author = {Hongming Huang and Peng Wang and Qiang Su and Hong Xu and Chun Jason Xue and André Brinkmann},
booktitle = {ASPLOS},
year = {2024},
doi = {10.1145/3620665.3640353}
}