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Shih-Jen Li

dblp:393/5226 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
0000-0003-2132-0743ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Storage systems · 77% Performance modeling and evaluation · 23%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Storage systems
i/o workload characterization
0.812024
A Stable Idle Time Detection Platform for Real I/O Workloads · ACM Trans. Archit. Code Optim. 2024

Methods — techniques the papers use, named apart from their topics

idle time detection methods · 0.8
YearPublicationVenuePosition
2024 A Stable Idle Time Detection Platform for Real I/O Workloads
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%.
Yen-Yu Lu, Chin-Hsien Wu, Shih-Jen Li, Cheng-Tze Lee, Cheng-Yen Wu
ACM Trans. Archit. Code Optim.3