SafeLoad: Efficient Admission Control Framework for Identifying Memory-Overloading Queries in Cloud Data Warehouses
Summary: SafeLoad targets memory-overloading query admission with interpretable filtering, hybrid global/cluster models, correction, and self-tuning quotas. SafeBench contributes 150M industrial queries; SafeLoad boosts precision 66% and cuts wasted CPU 8.09×. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Yifan Wu (Zhejiang University)
- 2. Yuhan Li (Alibaba)
- 3. Zhenhua Wang (Alibaba)
- 4. Zhongle Xie (Zhejiang University)
- 5. Dingyu Yang (Zhejiang University)
- 6. Ke Chen (Zhejiang University)
- 7. Lidan Shou (Zhejiang University)
- 8. Bo Tang (Southern University of Science and Technology)
- 9. Liang Lin (Alibaba)
- 10. Huan Li (Zhejiang University)
- 11. Gang Chen (Zhejiang University)
BibTeX Citation
@article{wu_vldb26,
title = {{SafeLoad: Efficient Admission Control Framework for Identifying Memory-Overloading Queries in Cloud Data Warehouses}},
author = {Wu, Yifan and Li, Yuhan and Wang, Zhenhua and Xie, Zhongle and Yang, Dingyu and Chen, Ke and Shou, Lidan and Tang, Bo and Lin, Liang and Li, Huan and Chen, Gang},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {9},
number = {4},
pages = {713--725},
doi = {10.14778/3785297.3785311},
url = {https://doi.org/10.14778/3785297.3785311},
year = {2026}
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Outgoing Citations (Sorted by Pagerank)
Showing 8 of 8 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 66 | The Snowflake Elastic Data Warehouse | 2016 | SIGMOD | 0.00038561587 |
| 682 | Robust Estimation of Resource Consumption for SQL Queries using Statistical Techniques | 2012 | VLDB | 0.00015014887 |
| 818 | Amazon Redshift Re-invented | 2022 | SIGMOD | 0.00013822916 |
| 2,563 | AnalyticDB: Real-time OLAP Database System at Alibaba Cloud | 2019 | VLDB | 8.412445e-05 |
| 3,809 | Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift | 2023 | SIGMOD | 7.1074195e-05 |
| 5,107 | Stage: Query Execution Time Prediction in Amazon Redshift | 2024 | SIGMOD | 6.3623786e-05 |
| 6,088 | How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks | 2025 | SIGMOD | 5.9813965e-05 |
| 8,079 | Bouncer: Admission Control with Response Time Objectives for Low-latency Online Data Systems | 2024 | SIGMOD | 5.4923802e-05 |
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