Model-Free Control for Distributed Stream Data Processing using Deep Reinforcement Learning
Summary: Model-free DRL control for distributed stream processing learns scheduling from limited runtime data without explicit models. On Apache Storm (continuous queries, log processing, word count) it reduces latency by 33.5% versus default and 14% versus model-based schedulers, with rapid online convergence. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Teng Li
- 2. Zhiyuan Xu
- 3. Jian Tang
- 4. Yanzhi Wang
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,076 | Learning a Partitioning Advisor for Cloud Databases | 2020 | SIGMOD | 7.6107677e-05 |
| 5,258 | One Model to Rule them All: Towards Zero-Shot Learning for Databases | 2022 | CIDR | 5.5998705e-05 |
| 5,368 | Fine-Grained Modeling and Optimization for Intelligent Resource Management in Big Data Processing | 2022 | VLDB | 5.5457532e-05 |
| 10,840 | Learned Cost Models for Query Optimization: From Batch to Streaming Systems | 2025 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 314 | MillWheel: Fault-Tolerant Stream Processing at Internet Scale | 2013 | VLDB | 0.00028084774 |
| 2,605 | Muppet: MapReduce-Style Processing of Fast Data | 2012 | VLDB | 8.4646171e-05 |
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