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Zhengtao Shen

dblp:419/9193 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none

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

Computer networks · 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 networks
1 paper
Network management and operations · 87% Datacenter networks · 13%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Network management and operations › network verification
data plane verification
0.912025
HeTu: High-Performance Centralized Parallel Data-Plane Verification for Hyper-Scale DCNs · ICNP 2025
Network management and operations
network verification
0.912025
HeTu: High-Performance Centralized Parallel Data-Plane Verification for Hyper-Scale DCNs · ICNP 2025
Datacenter networks › datacenter architecture
hyper-scale data center network
0.312025
HeTu: High-Performance Centralized Parallel Data-Plane Verification for Hyper-Scale DCNs · ICNP 2025

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

parallel verification · 0.9forwarding graph model · 0.9binary decision diagram · 0.9
YearPublicationVenuePosition
2025 HeTu: High-Performance Centralized Parallel Data-Plane Verification for Hyper-Scale DCNs
abstract
Existing data-plane verifiers face severe performance challenges in verifying hyper-scale underlay data center networks (DCNs) – centralized verifiers often fail to fully exploit the parallelism offered by the modern multi-core CPUs, while distributed verifiers suffer from high overhead due to task distribution and inter-node communication. To overcome these limitations, this paper introduces HeTu, a high-performance centralized parallel data-plane verifier specifically for verifying hyper-scale underlay DCNs. HeTu achieves ultra-fast verification through three key designs: (1) a fully parallel verification framework with small graph construction overhead, (2) a new binary decision diagram management strategy that enables full parallelism by using separated storage and selectively indexing and caching network-level predicates to reduce redundant operations, and (3) an optimized forwarding graph model that aggregates parallel tasks to eliminate redundant computation. Extensive evaluations on synthetic FatTree and large-scale production datasets show that HeTu outperforms state-of-the-art algorithms in runtime by 100× to 6000×, demonstrating its superior scalability and efficiency in data-plane verification of hyper-scale DCNs.
Zhengtao Shen, Feiyang Ding, Lizhao You, Weirong Jiang, Yongping Tang, Feng Luo 0006
ICNP3