Adaptive Filesystem Compression for Embedded
University of Michigan
Engineering and Computer Science
Lan S. Bai
Robert P. Dick
Embedded system secondary storage size is often constrained, yet storage
demands are growing as a result of increasing application complexity and
storage of personal data and multimedia files. Filesystem compression offers a
solution. This project formalizes the problem of automatic filesystem
compression using multiple compression algorithms. The average latency of
on-line file accesses is optimized under a constraint on filesystem capacity.
Our solution is based on predictive control. Predicted latency implications are
used to solve the file compression state selection problem using a multiple
choice knapsack problem formulation. This approach is evaluated on filesystem
traces and compared with other efficient heuristics. Our approach results in
34.1% reduction in file access latency compared to a straight-forward
heuristic that decompresses frequently-accessed files and compresses least
recently used files with more aggressive compression algorithms. It reduces
file access latency by 67.7% compared to uniformly compressing files
to the shallowest level required to meet storage capacity constraints.
Page maintained by Lan Bai.