How Can We Train Deep Learning Models Across Clouds and Continents? An Experimental Study

Summary: Experimental study of cross-cloud, cross-continent deep-learning training across spot VMs, on-premise, and hybrid deployments, quantifying cost/throughput tradeoffs for CV, NLP, and ASR. Distributed cheap spot instances can outperform centralized hardware and on-demand clouds. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID: 13557
Venue: VLDB
Year: 2024
Pagerank: 5.2634238e-05
Overall Rank: 9,469 | 35.04%
DOI: 10.14778/3648160.3648165

Incoming Non-self Citations Over Time

Authors

1. Alexander Erben (Technical University of Munich)
2. Ruben Mayer (University of Bayreuth)
3. Hans-Arno Jacobsen (University of Toronto)

BibTeX Citation

@article{erben_vldb24,
        title = {{How Can We Train Deep Learning Models Across Clouds and Continents? An Experimental Study}},
        author = {Erben, Alexander and Mayer, Ruben and Jacobsen, Hans-Arno},
        journal = {PVLDB},
        series = {{VLDB} '24},
        volume = {17},
        number = {6},
        pages = {1214--1226},
        doi = {10.14778/3648160.3648165},
        url = {https://doi.org/10.14778/3648160.3648165},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,769	Malleus: Straggler-Resilient Hybrid Parallel Training of Large-scale Models via Malleable Data and Model Parallelization	2025	SIGMOD	5.093636e-05

Outgoing Citations (Sorted by Pagerank)

Showing 1 of 1 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
521	PyTorch Distributed: Experiences on Accelerating Data Parallel Training	2020	VLDB	0.0001713368

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