Beaulieu, Jonathan2018-09-212018-09-212018-06https://hdl.handle.net/11299/200153University of Minnesota M.S. thesis.June 2018. Major: Computer Science. Advisor: Peter Peterson. 1 computer file (PDF); vi, 55 pages.File systems have utilized compression to increase the amount of storage available to the operating system. However, compression comes with the risk of increased latency and wasted computation when compressing incompressible data. We used Adaptive Compression (AC) to increase disk write speeds using compression, while not falling victim to incompressible data. Our AC system decides which algorithm to use from a list of available compression algorithms based on a model that queries CPU usage, disk bandwidth, current write queue length and estimates compressibility using “bytecounting,” an entropy-like statistic. Our model learns over time by building a table of estimated outcomes for each state and averaging previous outcomes for that state. We integrated our AC system into ZFSs write pipeline to reduce overall write times. In benchmarks with several standard datasets, our solution increased average write speeds by up to 48% compared to the baseline. For worst case (incompressible) data, our system decreased write speeds by 5%, due to system overhead. Compared to a previous ZFS AC system, our system does not perform statistically worse on any dataset and writes datasets with many small compressible files up to 49% faster.enadaptive compressionfilesystemZFSThesis or Dissertation