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Bodong Yan

dblp:387/3818 · DBLP profile ↗
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3ranked-venue papers
1as first author
3since 2021 · last 2026
—ORCID · none

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

Computer networks · 3 · 1 first-author · 3 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 networks
2 papers
Datacenter networks · 60% Transport protocols and congestion control · 21% Network measurement and analytics · 18%
Computer architecture, parallel and distributed computing, and storage systems
2 papers
High-performance computing · 54% Performance modeling and evaluation · 46%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Datacenter networks
RDMA
1.922026
Scalable, Low-Latency, and Hi-Precision Congestion Control in RDMA Datacenter Networks · IEEE Trans. Netw. 2026
Towards Lightweight Traffic Forecasting in RDMA Networks: Design and Application · INFOCOM 2025
Transport protocols and congestion control › low-latency transport
low-latency congestion control
1.012026
Scalable, Low-Latency, and Hi-Precision Congestion Control in RDMA Datacenter Networks · IEEE Trans. Netw. 2026
Datacenter networks › RDMA
RDMA congestion control
1.012026
Scalable, Low-Latency, and Hi-Precision Congestion Control in RDMA Datacenter Networks · IEEE Trans. Netw. 2026
Network measurement and analytics
traffic prediction
0.912025
Towards Lightweight Traffic Forecasting in RDMA Networks: Design and Application · INFOCOM 2025
High-performance computing
low-latency networking
0.312026
Scalable, Low-Latency, and Hi-Precision Congestion Control in RDMA Datacenter Networks · IEEE Trans. Netw. 2026
Performance modeling and evaluation
workload characterization
0.312025
Towards Lightweight Traffic Forecasting in RDMA Networks: Design and Application · INFOCOM 2025

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

congestion control · 2.0machine learning · 1.7
YearPublicationVenuePosition
2026 Scalable, Low-Latency, and Hi-Precision Congestion Control in RDMA Datacenter Networks
Sun Xu, Bodong Yan, Yangming Zhao, Jianchun Liu, Hongli Xu 0001
IEEE Trans. Netw.2
2025 Towards Lightweight Traffic Forecasting in RDMA Networks: Design and Application
Bodong Yan, Sun Xu, Bingyi Liu, Jianchun Liu
INFOCOM3
2024 LHCC: Low-Latency and Hi-Precision Congestion Control in RDMA Datacenter Networks
abstract
Congestion Control (CC) plays a vital role in deploying lossless datacenter networks based on Remote Direct Memory Access (RDMA). A high-performance CC scheme should provide low-latency and precise feedback to congestion events. However, no existing CC schemes achieved both features simultaneously. In this paper, we propose LHCC, a Low-latency and Hi-precision Congestion Control scheme for RDMA datacenter networks. LHCC uses out-band signaling to notify the network status and hence a packet sender can detect congestion events within an RTT. In addition, LHCC adjusts packet sending rate by taking into consideration all queues along the entire path that a packet has gone through. Accordingly, it provides a more precise CC compared with existing schemes especially when there are multiple bottlenecks in the networks. We build the LHCC prototype on a real testbed carrying NVIDIA BlueField-3 NICs and AGM39D FPGAs. Both testbed experiments and extensive simulations show that LHCC can reduce the Flow Completion Time (FCT) slow down and reduce the buffer usage (i.e., reduce the queue lengths) by up to 62.5% and 58%, respectively, compared with the state-of-the-art high-precision CC scheme, HPCC.
Bodong Yan, Yangming Zhao, Sun Xu, Jianchun Liu, Hongli Xu 0001
IWQoS1