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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)

Paper ID
13796
Venue
VLDB
Year
2024
Pagerank
5.093636e-05
Overall Rank
11,291 | 22.54%
DOI
10.14778/3685800.3685829

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Authors

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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