XORing Elephants: Novel Erasure Codes for Big Data
Summary: XORing Elephants proposes erasure codes with an optimal locality–minimum distance tradeoff for repair-efficient, higher reliability than Reed-Solomon. Implemented in Hadoop HDFS, they reduce repair disk I/O and network traffic ~2x, at 14% extra storage, achieving locality-optimal reliability beyond replication. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Maheswaran Sathiamoorthy (University of Southern California)
- 2. Alexandros G. Dimakis (University of Texas)
- 3. Megasthenis Asteris (University of Southern California)
- 4. Ramkumar Vadali (Meta)
- 5. Dimitris Papailiopoulos (University of Texas)
- 6. Scott Chen (Meta)
- 7. Dhruba Borthakur (Meta)
BibTeX Citation
@article{sathiamoorthy_vldb13,
title = {{XORing Elephants: Novel Erasure Codes for Big Data}},
author = {Sathiamoorthy, Maheswaran and Dimakis, Alexandros G. and Asteris, Megasthenis and Vadali, Ramkumar and Papailiopoulos, Dimitris and Chen, Scott and Borthakur, Dhruba},
journal = {PVLDB},
series = {{VLDB} '13},
volume = {6},
number = {5},
pages = {325--336},
doi = {10.14778/2535573.2488339},
url = {https://doi.org/10.14778/2535573.2488339},
year = {2013}
}Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,109 | The Case for Distributed Shared-Memory Databases with RDMA-Enabled Memory Disaggregation | 2023 | VLDB | 6.3615847e-05 |
| 6,958 | Efficient Fault Tolerance for Recommendation Model Training via Erasure Coding | 2023 | VLDB | 5.7303405e-05 |
| 8,001 | Pangea: Monolithic Distributed Storage for Data Analytics | 2019 | VLDB | 5.508791e-05 |
| 12,202 | Fatman: Cost-saving and reliable archival storage based on volunteer resources | 2014 | VLDB | 5.093636e-05 |
| 12,215 | Towards Building Wind Tunnels for Data Center Design | 2014 | VLDB | 5.093636e-05 |
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Showing 0 of 0 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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