Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift
Summary: Auto-WLM is ML-driven WLM for Redshift that auto-tunes concurrency and memory to maximize throughput under workloads. Locally trained query performance models predict runtime and memory to guide millions of scheduling decisions in real time. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Gaurav Saxena (Amazon)
- 2. Mohammad Rahman (Amazon)
- 3. Naresh Chainani (Amazon)
- 4. Chunbin Lin (Amazon; Visa)
- 5. George Caragea (Amazon; Lacework)
- 6. Fahim Chowdhury (Amazon)
- 7. Ryan Marcus (Amazon; University of Pennsylvania)
- 8. Tim Kraska (Amazon; Massachusetts Institute of Technology)
- 9. Ippokratis Pandis (Amazon)
- 10. Balakrishnan (Murali) Narayanaswamy (Amazon)
BibTeX Citation
@inproceedings{saxena_sigmod23,
title = {{Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift}},
author = {Saxena, Gaurav and Rahman, Mohammad and Chainani, Naresh and Lin, Chunbin and Caragea, George and Chowdhury, Fahim and Marcus, Ryan and Kraska, Tim and Pandis, Ippokratis and Narayanaswamy, Balakrishnan (Murali)},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3555041.3589677},
url = {https://dl.acm.org/doi/10.1145/3555041.3589677},
year = {2023}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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