Adaptive Filesystem Compression for Embedded Systems

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 flies. Filesystem compression offers a solution. This paper 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.