Optimization Strategies for A/B Testing on HADOOP
Summary: Load-aware, dependency- and inter-query-aware resource allocation accelerates concurrent Hive/MapReduce jobs for Hadoop A/B testing. Evaluated on eBay’s production cluster, it cuts runtime 233% versus sequential execution, with up to 40% further reduction from optimization. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Andrii Cherniak (University of Pittsburgh)
- 2. Huma Zaidi (eBay)
- 3. Vladimir Zadorozhny (University of Pittsburgh)
BibTeX Citation
@article{cherniak_vldb13,
title = {{Optimization Strategies for A/B Testing on HADOOP}},
author = {Cherniak, Andrii and Zaidi, Huma and Zadorozhny, Vladimir},
journal = {PVLDB},
series = {{VLDB} '13},
volume = {6},
number = {11},
pages = {973--984},
doi = {10.14778/2536222.2536227},
url = {https://doi.org/10.14778/2536222.2536227},
year = {2013}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 481 | Practical Skew Handling in Parallel Joins | 1992 | VLDB | 0.00017780716 |
| 735 | Profiling, What-if Analysis, and Cost-based Optimization of MapReduce Programs | 2011 | VLDB | 0.00014522606 |
| 923 | Starfish: A Self-tuning System for Big Data Analytics | 2011 | CIDR | 0.00013189886 |
| 3,714 | Large-Scale Machine Learning at Twitter | 2012 | SIGMOD | 7.1764857e-05 |
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