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Chengxin Xie

dblp:303/7642 · DBLP profile ↗
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2ranked-venue papers
1as first author
2since 2021 · last 2026
0000-0003-0814-4773ORCID · corroborated

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

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Systems, 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 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Storage systems › storage reliability
erasure coding
0.512021
Cluster-Aware Scattered Repair in Erasure-Coded Storage: Design and Analysis · IEEE Trans. Computers 2021
Storage systems
repair
0.512021
Cluster-Aware Scattered Repair in Erasure-Coded Storage: Design and Analysis · IEEE Trans. Computers 2021
Storage systems
storage reliability
0.512021
Cluster-Aware Scattered Repair in Erasure-Coded Storage: Design and Analysis · IEEE Trans. Computers 2021
Storage systems › distributed storage
storage cluster
0.112021
Cluster-Aware Scattered Repair in Erasure-Coded Storage: Design and Analysis · IEEE Trans. Computers 2021

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

simulation · 0.5cloud experimentation · 0.5
YearPublicationVenuePosition
2026 Bidirectional semantic accumulation for deep graph representation learning via autoencoders
Chengxin Xie, Qiya Song, Jingui Huang
Pattern Recognit.1
2021 Cluster-Aware Scattered Repair in Erasure-Coded Storage: Design and Analysis
abstract
Erasure coding is a storage-efficient means to guarantee data reliability in today's commodity storage systems, yet its repair performance is seriously hindered by the substantial repair traffic. Repair in clustered storage systems is even complicated because of the scarcity of the cross-cluster bandwidth. We present${\sf ClusterSR}$, a cluster-aware scattered repair approach.${\sf ClusterSR}$minimizes the cross-cluster repair traffic by carefully choosing the clusters for reading and repairing chunks. It further balances the cross-cluster repair traffic by scheduling the repair of multiple chunks. Large-scale simulation and Alibaba Cloud ECS experiments show that${\sf ClusterSR}$can reduce 5.6-52.7 percent of the cross-cluster repair traffic and improve 14.4–68.8 percent of the repair throughput.
Zhirong Shen, Shiyao Lin, Jiwu Shu, Chengxin Xie, Yingxun Fu
IEEE Trans. Computers4