Improving disk cache hit-ratios through cache partitioning

Thiébaut, Dominique; Stone, Harold S.; Wolf, Joel L.

doi:10.1109/12.144619

Cited by 82 publications

(41 citation statements)

References 13 publications

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“…Thiébaut et al [26] perform offline cache partitioning by finding a cache size such that hit rate derivatives are equal for all applications. Gniady et al [13] and Kim et al [14] partition the shared file system cache between multiple processes by detecting the access patterns.…”

Section: Related Workmentioning

confidence: 99%

Improving I/O performance using soft-QoS-based dynamic storage cache partitioning

Patrick¹,

Garg²,

Son

et al. 2009

2009 IEEE International Conference on Cluster Computing and Workshops

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Abstract-Resources are often shared to improve resource utilization and reduce costs. However, not all resources exhibit good performance when shared among multiple applications. The work presented here focuses on effectively managing a shared storage cache. To provide differentiated services to applications exercising a storage cache, we propose a novel scheme that uses curve fitting to dynamically partition the storage cache. Our scheme quickly adapts to application execution, showing increasing accuracy over time. It satisfies application QoS if it is possible to do so, maximizes the individual hit rates of the applications utilizing the cache, and consequently increases the overall storage cache hit rate. Through extensive trace-driven simulation, we show that our storage cache partitioning strategy not only effectively insulates multiple applications from one another but also provides QoS guarantees to applications over a long period of execution time. Using our partitioning strategy, we were able to increase the individual storage cache hit rates of the applications by 67% and 53% over the no-partitioning and equalpartitioning schemes, respectively. Additionally, we improved the overall cache hit rates of the entire storage system by 11% and 12.9% over the no-partitioning and equal-partitioning schemes, respectively, while meeting the QoS goals all the time.

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Section: Related Workmentioning

confidence: 99%

Improving I/O performance using soft-QoS-based dynamic storage cache partitioning

Patrick¹,

Garg²,

Son

et al. 2009

2009 IEEE International Conference on Cluster Computing and Workshops

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“…Cases without cache flushing are also tested. For the cases without cache flushing, both the complete model (Equation 21) and the approximation (Equation 23) are used to estimate the overall miss-rate. Based on the simulation results, the error caused by the approximation is discussed.…”

Section: Experimental Verificationmentioning

confidence: 99%

Malleable Caches

Devadas¹,

Rudolph²

2002

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“…Thiébaut, Stone and Wolf applied their partitioning work [14] to improve disk cache hit-ratios [20]. The model for tightly interleaved streams is extended to be applicable for more than two processes.…”

Section: Related Workmentioning

confidence: 99%