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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)
- Paper ID
- 13296
- Venue
- VLDB
- Year
- 2023
- Pagerank
- 4.2942813e-05
- Overall Rank
- 9,733 | 32.29%
- DOI
-
10.14778/3625054.3625064
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 20 of 20 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 183 |
Automatic Database Management System Tuning Through Large-scale Machine Learning |
2017 |
SIGMOD |
0.00036721403 |
| 288 |
Storm @Twitter |
2014 |
SIGMOD |
0.00028939871 |
| 424 |
Tuning Database Configuration Parameters with iTuned |
2009 |
VLDB |
0.00023616398 |
| 824 |
Twitter Heron: Stream Processing at Scale |
2015 |
SIGMOD |
0.0001623129 |
| 1,084 |
Dhalion: Self-Regulating Stream Processing in Heron |
2017 |
VLDB |
0.00014209714 |
| 1,226 |
Integrating Scale Out and Fault Tolerance in Stream Processing using Operator State Management |
2013 |
SIGMOD |
0.00013180799 |
| 1,902 |
Black or White? How to Develop an AutoTuner for Memory-based Analytics |
2020 |
SIGMOD |
0.00010157713 |
| 2,163 |
Elastic Machine Learning Algorithms in Amazon SageMaker |
2020 |
SIGMOD |
9.3949234e-05 |
| 2,338 |
Samza: Stateful Scalable Stream Processing at LinkedIn |
2017 |
VLDB |
9.00711e-05 |
| 3,522 |
ResTune: Resource Oriented Tuning Boosted by Meta-Learning for Cloud Databases |
2021 |
SIGMOD |
7.0096727e-05 |
| 3,550 |
Chi: A Scalable and Programmable Control Plane for Distributed Stream Processing Systems |
2018 |
VLDB |
6.9843512e-05 |
| 3,762 |
SABER: Window-Based Hybrid Stream Processing for Heterogeneous Architectures |
2016 |
SIGMOD |
6.7804471e-05 |
| 3,812 |
Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation |
2022 |
VLDB |
6.7373184e-05 |
| 4,044 |
Megaphone: Latency-conscious state migration for distributed streaming dataflows |
2019 |
VLDB |
6.4995312e-05 |
| 4,265 |
CGPTuner: a Contextual Gaussian Process Bandit Approach for the Automatic Tuning of IT Configurations Under Varying Workload Conditions |
2021 |
VLDB |
6.3097793e-05 |
| 4,795 |
Rhino: Efficient Management of Very Large Distributed State for Stream Processing Engines |
2020 |
SIGMOD |
5.9158043e-05 |
| 4,842 |
Towards Dynamic and Safe Configuration Tuning for Cloud Databases |
2022 |
SIGMOD |
5.8826802e-05 |
| 5,164 |
Distributed Operation in the Borealis Stream Processing Engine |
2005 |
SIGMOD |
5.6537475e-05 |
| 6,048 |
Load Shedding in Stream Databases: A Control-Based Approach |
2006 |
VLDB |
5.2365988e-05 |
| 6,629 |
A Holistic View of Stream Partitioning Costs |
2017 |
VLDB |
4.9880986e-05 |
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Model-Free Control for Distributed Stream Data Processing using Deep Reinforcement Learning |
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| 3,522 |
ResTune: Resource Oriented Tuning Boosted by Meta-Learning for Cloud Databases |
2021 |
SIGMOD |
7.0096727e-05 |
| 5,675 |
Staying FIT: Efficient Load Shedding Techniques for Distributed Stream Processing |
2007 |
VLDB |
5.3766e-05 |
| 4,265 |
CGPTuner: a Contextual Gaussian Process Bandit Approach for the Automatic Tuning of IT Configurations Under Varying Workload Conditions |
2021 |
VLDB |
6.3097793e-05 |
| 6,871 |
Towards General and Efficient Online Tuning for Spark |
2023 |
VLDB |
4.8997004e-05 |
| 4,842 |
Towards Dynamic and Safe Configuration Tuning for Cloud Databases |
2022 |
SIGMOD |
5.8826802e-05 |