APEROL: Adaptive Parallel Edge-to-cloud Runtime Optimization for Layered Workflow Execution
Summary: APEROL introduces a signature-based, parallel plan-enumeration framework for scalable operator placement across the cloud–edge continuum, offering exhaustive, heuristic, greedy and sampling optimizers to trade off speed vs. plan quality. Rapidly adapts to dynamic streams and churn (up to 2M candidate plans/sec) and proves effective on 5 workflows across tens–thousands of IoT sites. (summarized by gpt-5-mini on Mar 13 2026)
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Authors
- 1. Dimitrios Banelas (Technical University of Crete)
- 2. Alkis Simitsis (Athena Research Center)
- 3. Nikos Giatrakos (Technical University of Crete)
BibTeX Citation
@article{banelas_vldb26,
title = {{APEROL: Adaptive Parallel Edge-to-cloud Runtime Optimization for Layered Workflow Execution}},
author = {Banelas, Dimitrios and Simitsis, Alkis and Giatrakos, Nikos},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {2},
pages = {156--169},
doi = {10.14778/3773749.3773755},
url = {https://doi.org/10.14778/3773749.3773755},
year = {2026}
}
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