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Serving and Optimizing Machine Learning Workflows on Heterogeneous Infrastructures

Summary: JellyBean: jointly selects AutoML-generated model variants and places them across tiered heterogeneous infrastructure (edge/hubs/edge-DC/cloud) to meet SLOs (throughput, accuracy) while minimizing serving cost. Yields up to 58% cost reduction on VQA, 36% on vehicle tracking, and up to 5x cost savings versus cloud-only serving. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13265
Venue
VLDB
Year
2023
Pagerank
5.9986055e-05
Overall Rank
4,687 | 67.40%
DOI
10.14778/3570690.3570692

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