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Scarf: Self-Adaptive Tuning via Multi-Objective Reinforcement Learning for Apache Flink
Summary: Scarf: self-adaptive Flink knob tuning via multi-objective RL. Clusters workloads by knob sensitivity to cut sampling; learns offline Pareto-front “forest” of RL models optimizing throughput vs resource; transfers with GNN actor-critic + PNN warm-up for new topologies, yielding up to 62.5% CPU/68.3% memory savings and 77.1% faster tuning.
(summarized by gpt-5.4-mini on May 27 2026)
- Paper ID
- 14296
- Venue
- VLDB
- Year
- 2026
- Pagerank
- 4.1945683e-05
- Overall Rank
- 10,259 | 28.64%
- DOI
-
10.14778/3801059.3801066
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| Rank |
Citing Paper |
Year |
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Pagerank |
Outgoing Citations (Sorted by Pagerank)
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| 538 |
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2015 |
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2022 |
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0.00012146739 |
| 1,548 |
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2018 |
SIGMOD |
0.00011431383 |
| 2,659 |
Multi-Objective Parametric Query Optimization |
2015 |
VLDB |
8.3604734e-05 |
| 3,812 |
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2022 |
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6.7373184e-05 |
| 4,380 |
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6.2225151e-05 |
| 4,700 |
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2011 |
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5.9882572e-05 |
| 4,874 |
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2014 |
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2019 |
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