Runtime Measurements in the Cloud: Observing, Analyzing, and Reducing Variance
Summary: Month-long EC2 study spanning CPU, I/O, network, and MapReduce exposes large bimodal performance variance, tied to VM types and availability zones. Shows cloud noise threatens database wall-clock evaluations and SLAs, and offers measurement guidance. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jörg Schad (Saarland University)
- 2. Jens Dittrich (Saarland University)
- 3. Jorge-Arnulfo Quiané-Ruiz (Saarland University)
BibTeX Citation
@article{schad_vldb10,
title = {{Runtime Measurements in the Cloud: Observing, Analyzing, and Reducing Variance}},
author = {Schad, Jörg and Dittrich, Jens and Quiané-Ruiz, Jorge-Arnulfo},
journal = {PVLDB},
series = {{VLDB} '10},
volume = {3},
number = {1},
pages = {460--471},
doi = {10.14778/1920841.1920901},
url = {https://doi.org/10.14778/1920841.1920901},
year = {2010}
}
Incoming Citations (Sorted by Pagerank)
Showing 23 of 23 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 44 | A Comparison of Approaches to Large-Scale Data Analysis | 2009 | SIGMOD | 0.00046055057 |
| 1,348 | An Evaluation of Alternative Architectures for Transaction Processing in the Cloud | 2010 | SIGMOD | 0.00011071078 |
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