Work

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

Year: 2024

Type: article

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%.

Source: ACM Transactions on Architecture and Code Optimization

DOI: 10.1145/3695871

Authors: Yen-Yu Lu, Chin-Hsien Wu, Shih-Jen Li, Cheng-Tze Lee, Cheng-Yen Wu

Institution: National Taiwan University of Science and Technology

Language: English

Topic: Advanced Data Storage Technologies

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funder: National Science Council

Award: G5760726435