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

It is important to utilize the idle time of a workload to improve the system performance. In the article, 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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Publication Details

Journal
ACM Transactions on Architecture and Code Optimization
Published
2024-09-13
DOI
https://doi.org/10.1145/3695871
Primary Topic
Advanced Data Storage Technologies
Type
article
Field-Weighted Citation Impact
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article

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

Cheng-Tze Lee, Shih-Jen Li, Cheng-Yen Wu, Yen-Yu Lu et al.
ACM Transactions on Architecture and Code Optimization
Advanced Data Storage Technologies
article

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

Cheng-Tze Lee, Shih-Jen Li, Cheng-Yen Wu, Yen-Yu Lu, Chin-Hsien Wu
article en

Abstract

It is important to utilize the idle time of a workload to improve the system performance. In the article, 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%.

ACM Transactions on Architecture and Code OptimizationVol. 21(4)
National Taiwan University of Science and Technology (TW)
Openalex Percentile: Top 83%
Advanced Data Storage Technologies
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