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
- 2. Wei Lu
- 3. Zhibo Zhu
- 4. Xingyu Lu
- 5. Jun Zhou
- 6. Xiaojin Wang
- 7. Kangyu Liu
- 8. Kefan Wang
- 9. Renen Sun
- 10. Haiqing Wang
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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 |
|---|---|---|---|---|
| 1,322 | Automated Demand-driven Resource Scaling in Relational Database-as-a-Service | 2016 | SIGMOD | 0.00012610912 |
| 3,869 | MagicScaler: Uncertainty-aware, Predictive Autoscaling | 2023 | VLDB | 6.6802432e-05 |
| 6,110 | Doppler: Automated SKU Recommendation in Migrating SQL Workloads to the Cloud | 2022 | VLDB | 5.2056003e-05 |
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