Fast Checkpoint Recovery Algorithms for Frequently Consistent Applications
Summary: Exploits frequent consistency points in in-memory, high-throughput apps to drive novel checkpoint recovery. No locking or bulk copies; trades added in-memory space for dramatically lower overhead and latency, delivering near-constant latency and 10x reduction at low to medium updates (2x under heavy load). (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Tuan Cao (Cornell University)
- 2. Marcos Vaz Salles (Cornell University)
- 3. Benjamin Sowell (Cornell University)
- 4. Yao Yue (Cornell University)
- 5. Alan Demers (Cornell University)
- 6. Johannes Gehrke (Cornell University)
- 7. Walker White (Cornell University)
BibTeX Citation
@inproceedings{cao_sigmod11,
title = {{Fast Checkpoint Recovery Algorithms for Frequently Consistent Applications}},
author = {Cao, Tuan and Salles, Marcos Vaz and Sowell, Benjamin and Yue, Yao and Demers, Alan and Gehrke, Johannes and White, Walker},
series = {{SIGMOD} '11},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/1989323.1989352},
url = {https://dl.acm.org/doi/10.1145/1989323.1989352},
year = {2011}
}
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