Aether: A Scalable Approach to Logging
Summary: Aether scales ARIES logging on multi-core DBs; bottlenecks: tiny I/O bursts, flush lock waits, OS scheduler churn, memory contention. Holistic tech yields 20–69% speedups on log workloads and >1.8 GB/s small-log inserts on server, beating mutex-based logging. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Ryan Johnson (Carnegie Mellon University; EPFL)
- 2. Ippokratis Pandis (Carnegie Mellon University; EPFL)
- 3. Radu Stoica (EPFL)
- 4. Anastasia Ailamaki (Carnegie Mellon University; EPFL)
- 5. Manos Athanassoulis (EPFL)
BibTeX Citation
@article{johnson_vldb10,
title = {{Aether: A Scalable Approach to Logging}},
author = {Johnson, Ryan and Pandis, Ippokratis and Stoica, Radu and Ailamaki, Anastasia and Athanassoulis, Manos},
journal = {PVLDB},
series = {{VLDB} '10},
volume = {3},
number = {1},
pages = {681--692},
doi = {10.14778/1920841.1920928},
url = {https://doi.org/10.14778/1920841.1920928},
year = {2010}
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