Intelligent, adaptive file system policy selection

Traditionally, maximizing input/output performance has required tailoring application input/output patterns to the idiosyncrasies of specific input/output systems. The authors show that one can achieve high application input/output performance via a low overhead input/output system that automatically recognizes file access patterns and adaptively modifies system policies to match application requirements. This approach reduces the application developer´s input/output optimization effort by isolating input/output optimization decisions within a retargetable file system infrastructure. To validate these claims, they have built a lightweight file system policy testbed that uses a trained learning mechanism to recognize access patterns. The file system then uses these access pattern classifications to select appropriate caching strategies, dynamically adapting file system policies to changing input/output demands throughout application execution. The experimental data show dramatic speedups on both benchmarks and input/output intensive scientific applications.