Minjeong Yuk

dblp:346/2489 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
0000-0002-4328-7078ORCID · 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 · 93% Memory systems · 7%

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

TopicWeightPapersLastEvidence papers
Storage systems
flash and SSD
0.712023
All-Flash Array Key-Value Cache for Large Objects · EuroSys 2023
Storage systems › key-value storage
flash-based key-value caching
0.712023
All-Flash Array Key-Value Cache for Large Objects · EuroSys 2023
Storage systems
key-value storage
0.712023
All-Flash Array Key-Value Cache for Large Objects · EuroSys 2023
Storage systems › flash and SSD
SSD array
0.712023
All-Flash Array Key-Value Cache for Large Objects · EuroSys 2023
Memory systems
cache
0.212023
All-Flash Array Key-Value Cache for Large Objects · EuroSys 2023

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

collision-tolerant hashing · 0.7approximate metadata · 0.7
YearPublicationVenuePosition
2023 All-Flash Array Key-Value Cache for Large Objects
abstract
We present BigKV, a key-value cache specifically designed for caching large objects in an all-flash array (AFA). The design of BigKV is centered around the unique property of a cache: since it contains a copy of the data, exact bookkeeping of what is in the cache is not critical for correctness. By ignoring hash collisions, approximating metadata information, and allowing data loss from failures, BigKV significantly increases the cache hit ratio and keeps more useful objects in the system. Experiments on a real AFA show that our design increases the throughput by 3.1× on average and reduces the average and tail latency by 57% and 81%, respectively.
Jinhyung Koo, Jinwook Bae, Minjeong Yuk, Seonggyun Oh, Jungwoo Kim 0004, Jung-Soo Park, Bryan S. Kim, Sungjin Lee 0001
EuroSys3