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Yifei Chen 0011

dblp:75/5017-11 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
0009-0000-8388-5120ORCID · verified

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
Memory systems · 50% Storage systems · 50%

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

TopicWeightPapersLastEvidence papers
Storage systems › data reduction › data deduplication
inline deduplication
0.812024
FSDedup: Feature-Aware and Selective Deduplication for Improving Performance of Encrypted Non-Volatile Main Memory · ACM Trans. Storage 2024
Memory systems
non-volatile memory
0.812024
FSDedup: Feature-Aware and Selective Deduplication for Improving Performance of Encrypted Non-Volatile Main Memory · ACM Trans. Storage 2024

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

prefetch cache · 0.8error-correcting codes · 0.8cache refresh · 0.8
YearPublicationVenuePosition
2024 FSDedup: Feature-Aware and Selective Deduplication for Improving Performance of Encrypted Non-Volatile Main Memory
abstract
Enhancing the endurance, performance, and energy efficiency of encrypted Non-Volatile Main Memory (NVMM) can be achieved by minimizing written data through inline deduplication. However, existing approaches applying inline deduplication to encrypted NVMM suffer from substantial performance degradation due to high computing, memory footprint, and index-lookup overhead to generate, store, and query the cryptographic hash (fingerprint). In the preliminary ESD [ 14 ], we proposed the Error Correcting Code (ECC) assisted selective deduplication scheme, utilizing the ECC information as a fingerprint to identify similar data effectively and then leveraging the selective deduplication technique to eliminate a large amount of redundant data with high reference counts. In this article, we proposed FSDedup. Compared with ESD, FSDedup could leverage the prefetch cache to reduce the read overhead during similarity comparison and utilize the cache refresh mechanism to identify further and eliminate more redundant data. Extensive experimental evaluations demonstrate that FSDedup can enhance the performance of the NVMM system further than the ESD. Experimental results show that FSDedup can improve both write and read speed by up to 1.8×, enhance Instructions Per Cycle by up to 1.5×, and reduce energy consumption by up to 2.0×, compared to ESD.
Chunfeng Du, Zihang Lin, Suzhen Wu, Yifei Chen 0011, Jiapeng Wu, Shengzhe Wang 0001, Weichun Wang 0002, Bo Mao 0003
ACM Trans. Storage4