Scarf: Self-Adaptive Tuning via Multi-Objective Reinforcement Learning for Apache Flink
Summary: Scarf applies multi-objective RL to jointly tune Flink throughput and resource usage, learning an offline Pareto frontier. Workload-sensitivity clustering and transferable GNN actor-critics enable fast knob selection and adaptation across dynamic, heterogeneous jobs. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Liu Liu (Zhejiang University)
- 2. Shenghao Gong (Zhejiang University)
- 3. Ziquan Fang (Zhejiang University)
- 4. Yunjun Gao (Zhejiang University)
BibTeX Citation
@article{liu_vldb26,
title = {{Scarf: Self-Adaptive Tuning via Multi-Objective Reinforcement Learning for Apache Flink}},
author = {Liu, Liu and Gong, Shenghao and Fang, Ziquan and Gao, Yunjun},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {7},
pages = {1516--1529},
doi = {10.14778/3801059.3801066},
url = {https://doi.org/10.14778/3801059.3801066},
year = {2026}
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