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Ruixiong Tan

dblp:354/8526 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
0009-0009-6149-5493ORCID · 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 · 100%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Storage systems › storage hierarchy
hybrid storage
0.712023
A Universal SMR-aware Cache Framework with Deep Optimization for DM-SMR and HM-SMR Disks · ACM Trans. Storage 2023
Storage systems › magnetic recording
shingled magnetic recording
0.712023
A Universal SMR-aware Cache Framework with Deep Optimization for DM-SMR and HM-SMR Disks · ACM Trans. Storage 2023
Storage systems › flash and SSD
SSD cache
0.712023
A Universal SMR-aware Cache Framework with Deep Optimization for DM-SMR and HM-SMR Disks · ACM Trans. Storage 2023
Storage systems
i/o optimization
0.212023
A Universal SMR-aware Cache Framework with Deep Optimization for DM-SMR and HM-SMR Disks · ACM Trans. Storage 2023

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

write amplification optimization · 0.7cache framework · 0.7
YearPublicationVenuePosition
2023 A Universal SMR-aware Cache Framework with Deep Optimization for DM-SMR and HM-SMR Disks
abstract
To satisfy the enormous storage capacities required for big data, data centers have been adopting high-density shingled magnetic recording (SMR) disks. However, the weak fine-grained random write performance of SMR disks caused by their inherent write amplification and unbalanced read–write performance poses a severe challenge. Many studies have proposed solid-state drive (SSD) cache systems to address this issue. However, existing cache algorithms, such as the least recently used (LRU) algorithm, which is used to optimize cache popularity, and the MOST algorithm, which is used to optimize the write amplification factor, cannot exploit the full performance of the proposed cache systems because of their inappropriate optimization objectives. This article proposes a new SMR-aware cache framework called SAC+ to improve SMR-based hybrid storage. SAC+ integrates the two dominant types of SMR drives—namely, drive-managed and host-managed SMR drives—and provides a universal framework implementation. In addition, SAC+ integrally combines the drive characteristics to optimize I/O performance. The results of evaluations conducted using real-world traces indicate that SAC+ reduces the I/O time by 36–93% compared with state-of-the-art algorithms.
Diansen Sun, Ruixiong Tan, Yunpeng Chai
ACM Trans. Storage2