A self-tuning client-side metadata prefetching scheme for wide area network file systems

Bing Wei, Limin Xiao, Yao Song, Guangjun Qin, Jinbin Zhu, Baicheng Yan, Chaobo Wang, Zhisheng Huo
2021 Science China Information Sciences  
Client-side metadata prefetching is commonly used in wide area network (WAN) file systems because it can effectively hide network latency. However, most existing prefetching approaches do not meet the various prefetching requirements of multiple workloads. They are usually optimized for only one specific workload and have no or harmful effects on other workloads. In this paper, we present a new self-tuning client-side metadata prefetching scheme that uses two different prefetching strategies
more » ... dynamically adapts to workload changes. It uses a directory-directed prefetching strategy to prefetch the related file metadata in the same directory, and a correlation-directed prefetching strategy to prefetch the related file metadata accessed across directories. A novel self-tuning mechanism is proposed to efficiently convert the prefetching strategy between directory-directed and correlation-directed prefetching. Experimental results using real system traces show that the hit ratio of the client-side cache can be significantly improved by our self-tuning client-side prefetching. With regards to the multi-workload concurrency scenario, our approach improves the hit ratios for the no-prefetching, directory-directed prefetching, variant probability graph algorithm, variant apriori algorithm, and variant semantic distance algorithm by up to 15.22%, 6.32%, 10.08%, 11.65%, and 10.73%, corresponding to 25. 24%, 18.11%, 23.53%, 24.94%, and 24.19% reductions in the average access time, respectively. Keywords wide area network file systems, multiple workloads, metadata prefetching, correlation-directed prefetching, directory-directed prefetching, self-tuning prefetching Citation Wei B, Xiao L M, Song Y, et al. A self-tuning client-side metadata prefetching scheme for wide area network file systems. Sci China Inf Sci, 2022, 65(3): 132101, https://doi.
doi:10.1007/s11432-019-2833-1 fatcat:h66h5astznfuxakkrtzzczhcua