Demo Data from EDBT 2026 / reviewers in the wild / expert
Lianju Qian
dblp:306/0329
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none
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 · 70% Cloud and datacenter computing · 23% Performance modeling and evaluation · 7% |
Topics — the 5 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Storage systems
flash and SSD |
0.5 | 1 | 2021 | Low I/O Intensity-aware Partial GC Scheduling to Reduce Long-tail Latency in SSDs · ACM Trans. Archit. Code Optim. 2021 |
Storage systems › storage performance
i/o latency |
0.5 | 1 | 2021 | Low I/O Intensity-aware Partial GC Scheduling to Reduce Long-tail Latency in SSDs · ACM Trans. Archit. Code Optim. 2021 |
Storage systems
storage reliability |
0.5 | 1 | 2021 | Low I/O Intensity-aware Partial GC Scheduling to Reduce Long-tail Latency in SSDs · ACM Trans. Archit. Code Optim. 2021 |
Cloud and datacenter computing › quality of service
tail latency |
0.5 | 1 | 2021 | Low I/O Intensity-aware Partial GC Scheduling to Reduce Long-tail Latency in SSDs · ACM Trans. Archit. Code Optim. 2021 |
Performance modeling and evaluation
workload characterization |
0.1 | 1 | 2021 | Low I/O Intensity-aware Partial GC Scheduling to Reduce Long-tail Latency in SSDs · ACM Trans. Archit. Code Optim. 2021 |
Methods — techniques the papers use, named apart from their topics
mathematical modeling · 0.5fourier transform · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Low I/O Intensity-aware Partial GC Scheduling to Reduce Long-tail Latency in SSDsabstractThis article proposes a low I/O intensity-aware scheduling scheme on garbage collection (GC) in SSDs for minimizing the I/O long-tail latency to ensure I/O responsiveness. The basic idea is to assemble partial GC operations by referring to several determinable factors (e.g., I/O characteristics) and dispatch them to be processed together in idle time slots of I/O processing. To this end, it first makes use of Fourier transform to explore the time slots having relative sparse I/O requests for conducting time-consuming GC operations, as the number of affected I/O requests can be limited. After that, it constructs a mathematical model to further figure out the types and quantities of partial GC operations, which are supposed to be dealt with in the explored idle time slots, by taking the factors of I/O intensity, read/write ratio, and the SSD use state into consideration. Through a series of simulation experiments based on several realistic disk traces, we illustrate that the proposed GC scheduling mechanism can noticeably reduce the long-tail latency by between 5.5% and 232.3% at the 99.99th percentile, in contrast to state-of-the-art methods. Zhibing Sha, Jun Li 0062, Lihao Song, Jiewen Tang, Min Huang 0018, Zhigang Cai, Lianju Qian, Jianwei Liao 0001, Zhiming Liu 0001 |
ACM Trans. Archit. Code Optim. | 7 |