PerfGuard: Deploying ML-for-Systems without Performance Regressions, Almost!
Summary: PerfGuard provides a pre-production safeguard for ML-for-systems, reducing deployment regressions. It confines search to query-plan deltas, learns delta-cost signals with a DL pipeline, and highlights key plan components, showing offline promise for relational DBs. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Remmelt Ammerlaan (Microsoft)
- 2. Gilbert Antonius (Microsoft)
- 3. Marc Friedman (Microsoft)
- 4. H M Sajjad Hossain (Microsoft)
- 5. Alekh Jindal (Microsoft)
- 6. Peter Orenberg (Microsoft)
- 7. Hiren Patel (Microsoft)
- 8. Shi Qiao (Microsoft)
- 9. Vijay Ramani (Microsoft)
- 10. Lucas Rosenblatt (Microsoft)
- 11. Abhishek Roy (Microsoft)
- 12. Irene Shaffer (Microsoft)
- 13. Soundarajan Srinivasan (Microsoft)
- 14. Markus Weimer (Microsoft)
BibTeX Citation
@article{ammerlaan_vldb21,
title = {{PerfGuard: Deploying ML-for-Systems without Performance Regressions, Almost!}},
author = {Ammerlaan, Remmelt and Antonius, Gilbert and Friedman, Marc and Hossain, H M Sajjad and Jindal, Alekh and Orenberg, Peter and Patel, Hiren and Qiao, Shi and Ramani, Vijay and Rosenblatt, Lucas and Roy, Abhishek and Shaffer, Irene and Srinivasan, Soundarajan and Weimer, Markus},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {13},
pages = {3362--3375},
doi = {10.14778/3484224.3484233},
url = {https://doi.org/10.14778/3484224.3484233},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
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