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%.
Authors
- Cheng-Tze Lee
- Shih-Jen Li (ORCID: https://orcid.org/0000-0003-2132-0743)
- Cheng-Yen Wu
- Yen-Yu Lu
- Chin-Hsien Wu (ORCID: https://orcid.org/0000-0002-8704-1483)
Institutions
- National Taiwan University of Science and Technology (TW)
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
- 0.00