From Worst-Case to Average-Case Analysis: Accurate Latency Predictions for Key-Value Storage Engines
Summary: Average-case latency analysis for storage engines, surpassing worst-case models. A distribution-aware framework predicts latency across diverse workloads and data structures; validated with tuning models on RocksDB and WiredTiger. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Meena Jagadeesan (Harvard University)
- 2. Garrett Tanzer (Harvard University)
BibTeX Citation
@inproceedings{jagadeesan_sigmod20,
title = {{From Worst-Case to Average-Case Analysis: Accurate Latency Predictions for Key-Value Storage Engines}},
author = {Jagadeesan, Meena and Tanzer, Garrett},
series = {{SIGMOD} '20},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3318464.3384408},
url = {https://dl.acm.org/doi/10.1145/3318464.3384408},
year = {2020}
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Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 43 | The Case for Learned Index Structures | 2018 | SIGMOD | 0.00046060254 |
| 446 | Monkey: Optimal Navigable Key-Value Store | 2017 | SIGMOD | 0.00018332392 |
| 831 | Dostoevsky: Better Space-Time Trade-Offs for LSM-Tree Based Key-Value Stores via Adaptive Removal of Superfluous Merging | 2018 | SIGMOD | 0.00013748512 |
| 1,616 | The Data Calculator*: Data Structure Design and Cost Synthesis from First Principles and Learned Cost Models | 2018 | SIGMOD | 0.00010213691 |
| 1,942 | Design Continuums and the Path Toward Self-Designing Key-Value Stores that Know and Learn | 2019 | CIDR | 9.4451535e-05 |
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