OptScaler: A Collaborative Framework for Robust Autoscaling in the Cloud
Summary: Collaborative autoscaling framework that tightly integrates proactive workload prediction and a reactive self‑tuning estimator via an optimization module using MPC with chance constraints, enabling robust joint decisions for co‑located long‑running apps. Outperforms prior autoscalers (~36% fewer SLO violations) and validated in production at Alipay. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Ding Zou (Ant Financial; Zhejiang University)
- 2. Wei Lu (Ant Financial)
- 3. Zhibo Zhu (Ant Financial)
- 4. Xingyu Lu (Ant Financial)
- 5. Jun Zhou (Ant Financial)
- 6. Xiaojin Wang (Ant Financial)
- 7. Kangyu Liu (Ant Financial)
- 8. Kefan Wang (Ant Financial)
- 9. Renen Sun (Ant Financial)
- 10. Haiqing Wang (Ant Financial)
BibTeX Citation
@article{zou_vldb24,
title = {{OptScaler: A Collaborative Framework for Robust Autoscaling in the Cloud}},
author = {Zou, Ding and Lu, Wei and Zhu, Zhibo and Lu, Xingyu and Zhou, Jun and Wang, Xiaojin and Liu, Kangyu and Wang, Kefan and Sun, Renen and Wang, Haiqing},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {12},
pages = {4090--4103},
doi = {10.14778/3685800.3685829},
url = {https://doi.org/10.14778/3685800.3685829},
year = {2024}
}
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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 |
|---|---|---|---|---|
| 1,092 | Automated Demand-driven Resource Scaling in Relational Database-as-a-Service | 2016 | SIGMOD | 0.00012221946 |
| 3,950 | MagicScaler: Uncertainty-aware, Predictive Autoscaling | 2023 | VLDB | 6.9980354e-05 |
| 6,177 | Doppler: Automated SKU Recommendation in Migrating SQL Workloads to the Cloud | 2022 | VLDB | 5.9496208e-05 |
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