ContTune: Continuous Tuning by Conservative Bayesian Optimization for Distributed Stream Data Processing Systems
Summary: ContTune continuously tunes operator parallelism in distributed stream-processing DAGs by decoupling topology via a Big phase that decomposes jobs into concurrent subproblems. Its Small phase applies Conservative Bayesian Optimization that reuses past observations and uses SOTA tuning as conservative exploration to speed tuning and avoid SLA violations, reducing reconfigurations by ~58–61%. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jinqing Lian (Beijing Institute of Technology)
- 2. Xinyi Zhang (Peking University)
- 3. Yingxia Shao (Beijing Institute of Technology)
- 4. Zenglin Pu (Beijing Institute of Technology)
- 5. Qingfeng Xiang (Beijing Institute of Technology)
- 6. Yawen Li (Beijing Institute of Technology)
- 7. Bin Cui (Peking University)
BibTeX Citation
@article{lian_vldb23,
title = {{ContTune: Continuous Tuning by Conservative Bayesian Optimization for Distributed Stream Data Processing Systems}},
author = {Lian, Jinqing and Zhang, Xinyi and Shao, Yingxia and Pu, Zenglin and Xiang, Qingfeng and Li, Yawen and Cui, Bin},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {13},
pages = {4282--4295},
doi = {10.14778/3625054.3625064},
url = {https://doi.org/10.14778/3625054.3625064},
year = {2023}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,304 | An Efficient Transfer Learning Based Configuration Adviser for Database Tuning | 2024 | VLDB | 6.1758143e-05 |
| 10,800 | Scarf: Self-Adaptive Tuning via Multi-Objective Reinforcement Learning for Apache Flink | 2026 | VLDB | 4.9605343e-05 |
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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