Uniform scheduling of interruptible garbage collection and request IO to improve performance and wear-leveling of SSDs

Yao, Yingbiao; Kong, Xiaochong; Bao, Jiecheng; Xu, Xiaojun; Gu, Nong; Feng, Wei

doi:10.1007/s11227-021-04294-2

J Supercomput

2022

DOI: 10.1007/s11227-021-04294-2

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Uniform scheduling of interruptible garbage collection and request IO to improve performance and wear-leveling of SSDs

Yingbiao Yao

Xiaochong Kong

Jiecheng Bao

et al.

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A Stable Idle Time Detection Platform for Real I/O Workloads

Lu,

Wu,

et al. 2024

ACM Trans. Archit. Code Optim.

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It is important to utilize the idle time of a workload to improve the system performance. In the paper, we will explore multiple idle time detection methods to predict the idle time of the real I/O workloads. The objective is to build a stable idle time detection platform by investigating the impact of multiple representative methods to pursue a more stable prediction accuracy. The experimental results show that the prediction accuracy of the proposed platform can be stable between 60% and 80%.

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A Stable Idle Time Detection Platform for Real I/O Workloads

Lu,

Wu,

et al. 2024

ACM Trans. Archit. Code Optim.

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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Uniform scheduling of interruptible garbage collection and request IO to improve performance and wear-leveling of SSDs

Cited by 1 publication

References 29 publications

A Stable Idle Time Detection Platform for Real I/O Workloads

A Stable Idle Time Detection Platform for Real I/O Workloads

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