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Model-Free Control for Distributed Stream Data Processing using Deep Reinforcement Learning

Summary: Introduces the first model-free deep-RL controller for distributed stream scheduling, learning workload placement from sparse runtime statistics without queueing models. Implemented in Storm, it jointly optimizes placement and communication effects, cutting tuple latency 33.5% over default scheduling. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
11965
Venue
VLDB
Year
2018
Pagerank
5.6766532e-05
Overall Rank
7,194 | 50.65%
DOI
10.14778/3184470.3184474

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{li_vldb18,
        title = {{Model-Free Control for Distributed Stream Data Processing using Deep Reinforcement Learning}},
        author = {Li, Teng and Xu, Zhiyuan and Tang, Jian and Wang, Yanzhi},
        journal = {PVLDB},
        series = {{VLDB} '18},
        volume = {11},
        number = {6},
        pages = {705--718},
        doi = {10.14778/3184470.3184474},
        url = {https://doi.org/10.14778/3184470.3184474},
        year = {2018}
}

Incoming Citations (Sorted by Pagerank)

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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
224 MillWheel: Fault-Tolerant Stream Processing at Internet Scale 2013 VLDB 0.00024130894
2,357 Muppet: MapReduce-Style Processing of Fast Data 2012 VLDB 8.7013171e-05
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