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Intelligent, adaptive file system policy selection

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2 Author(s)
Madhyastha, T.M. ; Dept. of Comput. Sci., Illinois Univ., Urbana, IL, USA ; Reed, D.A.

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.

Published in:

Frontiers of Massively Parallel Computing, 1996. Proceedings Frontiers '96., Sixth Symposium on the

Date of Conference:

27-31 Oct. 1996