Cosine: A Cloud-Cost Optimized Self-Designing Key-Value Storage Engine
Summary: Cosine automatically synthesizes cloud-cost/performance-optimized key-value engines from a 10^36-design space spanning LSMs, B-trees, hash tables, and hybrids. Distribution-aware I/O and learned CPU models enable second-scale search and Rust code generation, yielding up to 53× gains over established engines. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Subarna Chatterjee (Harvard University)
- 2. Meena Jagadeesan (Harvard University)
- 3. Wilson Qin (Harvard University)
- 4. Stratos Idreos (Harvard University)
BibTeX Citation
@article{chatterjee_vldb22,
title = {{Cosine: A Cloud-Cost Optimized Self-Designing Key-Value Storage Engine}},
author = {Chatterjee, Subarna and Jagadeesan, Meena and Qin, Wilson and Idreos, Stratos},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {1},
pages = {112--126},
doi = {10.14778/3485450.3485461},
url = {https://doi.org/10.14778/3485450.3485461},
year = {2022}
}
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