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Liulan Qin

dblp:258/9795 · DBLP profile ↗
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2ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none

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

Systems, architecture and hardware · 2 · 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 networks
1 paper
Datacenter networks · 93% Internet architecture and protocols · 7%

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

TopicWeightPapersLastEvidence papers
Datacenter networks
lossless network
0.412020
P-PFC: Reducing Tail Latency with Predictive PFC in Lossless Data Center Networks · IEEE Trans. Parallel Distributed Syst. 2020
Datacenter networks › lossless ethernet
priority flow control
0.412020
P-PFC: Reducing Tail Latency with Predictive PFC in Lossless Data Center Networks · IEEE Trans. Parallel Distributed Syst. 2020
Datacenter networks
RDMA
0.412020
P-PFC: Reducing Tail Latency with Predictive PFC in Lossless Data Center Networks · IEEE Trans. Parallel Distributed Syst. 2020
Datacenter networks › low-latency networking
tail latency reduction
0.412020
P-PFC: Reducing Tail Latency with Predictive PFC in Lossless Data Center Networks · IEEE Trans. Parallel Distributed Syst. 2020
Internet architecture and protocols
buffer management
0.112020
P-PFC: Reducing Tail Latency with Predictive PFC in Lossless Data Center Networks · IEEE Trans. Parallel Distributed Syst. 2020

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

proactive PFC triggering · 0.4derivative-based prediction · 0.4
YearPublicationVenuePosition
2021 Django: Bilateral coflow scheduling with predictive concurrent connections
Jiaqi Zheng 0001, Liulan Qin, Bingchuan Tian, Chen Tian 0001, Bo Li 0061, Guihai Chen
J. Parallel Distributed Comput.2
2020 P-PFC: Reducing Tail Latency with Predictive PFC in Lossless Data Center Networks
abstract
Remote Direct Memory Access(RDMA) technology rapidly changes the landscape of nowadays datacenter applications. Congestion control for RDMA networking is a critical challenge. As an end-to-end layer 3 congestion control mechanism, Datacenter QCN (DCQCN) alleviates the unfairness and head-of-the-line blocking problems of Priority-based Flow Control (PFC). However, a lossless network does not guarantee low latency even with DCQCN enabled. When network congestion happens, switch queues still build-up due to the response latency of end-to-end solutions. In this article, we propose Predictive PFC (P-PFC) to reduce tail latency in RDMA networks. P-PFC monitors the derivative of buffer occupation, predicts the happening of PFC trigger in the future, and proactively triggers PFC pause in advance. The benefit is that buffer usage can be maintained at a low level, hence the tail latency can be controlled. Preliminary evaluation results demonstrate that P-PFC can reduce tail latency by more than half of that in standard PFC in many scenarios, without hurting the throughput and average latency. P-PFC can also protect innocent flows compared with standard PFC according to our experiments. To our best knowledge, this is the first work of using derivative to improve PFC in lossless RDMA networks.
Chen Tian 0001, Bo Li 0061, Liulan Qin, Jiaqi Zheng 0001, Wei Wang 0002, Guihai Chen, Wan-Chun Dou
IEEE Trans. Parallel Distributed Syst.3