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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
- 14297
- Venue
- VLDB
- Year
- 2026
- Pagerank
- 4.1905499e-05
- Overall Rank
- 10,259 | 28.70%
- 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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2017 |
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| 510 |
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0.00021420477 |
| 536 |
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2015 |
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2019 |
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2017 |
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0.00014201838 |
| 1,226 |
Integrating Scale Out and Fault Tolerance in Stream Processing using Operator State Management |
2013 |
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| 1,404 |
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2022 |
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0.00012179714 |
| 1,546 |
Structured Streaming: A Declarative API for Real-Time Applications in Apache Spark |
2018 |
SIGMOD |
0.00011418993 |
| 2,652 |
Multi-Objective Parametric Query Optimization |
2015 |
VLDB |
8.3662031e-05 |
| 3,655 |
Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation |
2022 |
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6.8723042e-05 |
| 4,180 |
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6.3725334e-05 |
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HUNTER: An Online Cloud Database Hybrid Tuning System for Personalized Requirements |
2022 |
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6.2331947e-05 |
| 4,698 |
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2011 |
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5.9835195e-05 |
| 4,730 |
UDO: Universal Database Optimization using Reinforcement Learning |
2021 |
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5.9604983e-05 |
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4.2901665e-05 |
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2019 |
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4.2901665e-05 |
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