ALACC: Accelerating Restore Performance of Data Deduplication Systems Using Adaptive Look-Ahead Window Assisted Chunk Caching


Zhichao Cao, Hao Wen, Fenggang Wu, and David H.C. Du, Department of Computer Science, University of Minnesota, Twin Cities


Data deduplication has been widely applied in storage systems to improve the efficiency of space utilization. In data deduplication systems, the data restore performance is seriously hindered by read amplification since the accessed data chunks are scattered over many containers. A container consisting of hundreds or thousands data chunks is the data unit to be read from or write to the storage. Several schemes such as forward assembly, container-based caching, and chunk-based caching are used to reduce the number of container-reads during the restore process. However, how to effectively use these schemes to get the best restore performance is still unclear.

In this paper, we first study the trade-offs of using these schemes in terms of read amplification and computing time. We then propose a combined data chunk caching and forward assembly scheme called ALACC (Adaptive Look-Ahead Chunk Caching) for improving restore performance which can adapt to different deduplication workloads with a fixed total amount of memory. This is accomplished by extending and shrinking the look-ahead window adaptively to cover an appropriate data recipe range and dynamically deciding the memory to be allocated to forward assembly area and chunk-based caching. Our evaluations show the restore throughput of ALACC is higher than that of the optimum case of various configurations using the fixed amount of memory allocated to forward assembly and to chunk-based caching.

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@inproceedings {210502,
author = {zhichao Cao and Hao Wen and Fenggang Wu and David H.C. Du},
title = {{ALACC}: Accelerating Restore Performance of Data Deduplication Systems Using Adaptive Look-Ahead Window Assisted Chunk Caching},
booktitle = {16th {USENIX} Conference on File and Storage Technologies ({FAST} 18)},
year = {2018},
isbn = {978-1-931971-42-3},
address = {Oakland, CA},
pages = {309--324},
url = {},
publisher = {{USENIX} Association},