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NeutronCloud: Resource-Aware Distributed GNN Training in Fluctuating Cloud Environments

Summary: NeutronCloud aligns per-worker GNN workloads to fluctuating cloud resources by dynamically tuning a hybrid local-vs-remote dependency retrieval ratio. It uses dependency-aware partial-reduce to reuse historical embeddings and skip stragglers, achieving 1.8–4.4× speedups. (summarized by gpt-5-mini on Mar 13 2026)

Paper ID
14354
Venue
VLDB
Year
2026
Pagerank
4.1905499e-05
Overall Rank
10,310 | 28.35%
DOI
10.14778/3772181.3772186

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
271 AliGraph: A Comprehensive Graph Neural Network Platform 2019 VLDB 0.00029565193
1,162 Sancus: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks 2022 VLDB 0.00013573136
1,332 AGL: A Scalable System for Industrial-purpose Graph Machine Learning 2020 VLDB 0.00012549751
2,399 ByteGNN: Efficient Graph Neural Network Training at Large Scale 2022 VLDB 8.8869693e-05
3,028 NeutronStar: Distributed GNN Training with Hybrid Dependency Management 2022 SIGMOD 7.6833093e-05
3,092 Scalable and Efficient Full-Graph GNN Training for Large Graphs 2023 SIGMOD 7.5869574e-05
5,332 Heterogeneity-Aware Distributed Machine Learning Training via Partial Reduce 2021 SIGMOD 5.5640779e-05
7,087 HongTu: Scalable Full-Graph GNN Training on Multiple GPUs 2023 SIGMOD 4.8324242e-05
8,117 SDPipe: A Semi-Decentralized Framework for Heterogeneity-aware Pipeline-parallel Training 2023 VLDB 4.5788485e-05
10,027 NeutronHeter: Optimizing Distributed Graph Neural Network Training for Heterogeneous Clusters 2026 SIGMOD 4.1905499e-05
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