Pacaca: Mining Object Correlations and Parallelism for Enhancing User Experience with Cloud Storage

Hou, Binbing; Feng, Chen

doi:10.1109/mascots.2018.00036

Cited by 6 publications

(1 citation statement)

References 29 publications

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“…Therefore, it provides better access performance than common prefetching. Pacaca [23] is a client-side cache management framework that integrates object clustering, parallelized prefetching, and cost-aware caching to achieve a high level of cloud storage performance. However, the current prefetching schemes conducted on file servers cannot dynamically adapt to workload changes; they fail to hide the latency caused by network communications when facing multiple workloads.…”

Section: Related Workmentioning

confidence: 99%

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

Wei

Xiao

Song

et al. 2021

Sci. China Inf. Sci.

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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 and 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.

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Section: Related Workmentioning

confidence: 99%