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Depei Qian
Researcher at Beihang University
Publications - 289
Citations - 2017
Depei Qian is an academic researcher from Beihang University. The author has contributed to research in topics: Wireless sensor network & Speedup. The author has an hindex of 21, co-authored 269 publications receiving 1699 citations. Previous affiliations of Depei Qian include Sun Yat-sen University & Xi'an Jiaotong University.
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Journal ArticleDOI
Congestion avoidance, detection and alleviation in wireless sensor networks
TL;DR: A novel scheme for congestion avoidance, detection and alleviation (CADA) in WSNs is proposed, where a small number of representative nodes are chosen from those in the event area as data sources so that the source traffic can be suppressed proactively to avoid potential congestion.
Journal ArticleDOI
The Deep Learning Compiler: A Comprehensive Survey
Mingzhen Li,Yi Liu,Xiaoyan Liu,Qingxiao Sun,Xin You,Hailong Yang,Zhongzhi Luan,Lin Gan,Guangwen Yang,Depei Qian +9 more
TL;DR: This article performs a comprehensive survey of existing DL compilers by dissecting the commonly adopted design in details, with emphasis on the DL oriented multi-level IRs, and frontend/backend optimizations.
Proceedings ArticleDOI
ERMS: An Elastic Replication Management System for HDFS
TL;DR: ERMS provides an active/standby storage model for HDFS that utilizes a complex event processing engine to distinguish real-time data types, and then dynamically increases extra replicas for hot data, cleans up these extra Replicas when the data cool down, and uses erasure codes for cold data.
Journal ArticleDOI
MapReduce Workload Modeling with Statistical Approach
TL;DR: This paper proposes a statistical analysis approach to identify the relationships among workload characteristics, Hadoop configurations and workload performance, and applies principal component analysis and cluster analysis to 45 different metrics, which derive relationships between workload characteristics and corresponding performance under different Hadoan configurations.
Proceedings ArticleDOI
Virtual machine mapping policy based on load balancing in private cloud environment
TL;DR: This paper presents a virtual machine mapping policy based on multi-resource load balancing that uses the resource consumption of the running virtual machine and the self-adaptive weighted approach to ease the problem of load crowding in the concurrent users scene.