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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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 102 | The Case for Learned Index Structures | 2018 | SIGMOD | 0.00049545203 |
| 609 | Monkey: Optimal Navigable Key-Value Store | 2017 | SIGMOD | 0.0001923446 |
| 1,311 | Dostoevsky: Better Space-Time Trade-Offs for LSM-Tree Based Key-Value Stores via Adaptive Removal of Superfluous Merging | 2018 | SIGMOD | 0.00012657439 |
| 2,157 | The Data Calculator*: Data Structure Design and Cost Synthesis from First Principles and Learned Cost Models | 2018 | SIGMOD | 9.416022e-05 |
| 2,606 | Design Continuums and the Path Toward Self-Designing Key-Value Stores that Know and Learn | 2019 | CIDR | 8.4645832e-05 |
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