Common Pitfalls with Data Deduplication Parameters and Metrics
Luke Schultz, Owen Randall, Paul Lu · 2024
Content-defined chunking (CDC) based data deduplication is a complex process, leading to the use of rule-of-thumb approaches and standardized parameter values. However, our work challenges these standard approaches which can lead to worse deduplication ratios, and reemphasizes that parameters need to be optimized for each dataset. We expose new pitfalls, analyze the behaviour of the underlying deduplication process, and provide solutions to aid future deduplication work.Deduplication research often solely reports the expected chunk length of their system without providing the low-level parameters. Our results show that expected chunk length is inadequate for properly describing deduplication, making empirical reproducibility challenging. In fact, different parameter sets with the same expected chunk length can yield different deduplication ratios. We further show that this discrepancy can be explained by chunk length variance.We find that because expected average and standard deviation of chunk length do not account for file boundaries and fingerprint value noise, they can significantly differ from observed values. We show that these phenomena can also cause the same parameter set to give different chunk length distributions on different datasets, explaining why parameters must be tuned to each dataset. However, we find on our datasets that the maximum chunk length parameter does not need to be tuned, and that a rule-of-thumb value of 216is a reasonable selection. We provide our datasets in full to promote reproducibility and further work.