Mammoth : gearing Hadoop towards memory-intensive MapReduce applications
- Submitting institution
-
The University of Warwick
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 5999
- Type
- D - Journal article
- DOI
-
10.1109/TPDS.2014.2345068
- Title of journal
- IEEE Transactions on Parallel and Distributed Systems
- Article number
- -
- First page
- 2300
- Volume
- 26
- Issue
- 8
- ISSN
- 1045-9219
- Open access status
- Out of scope for open access requirements
- Month of publication
- August
- Year of publication
- 2015
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
7
- Research group(s)
-
D - Data Science, Systems and Security
- Citation count
- 20
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Published and selected as a "spotlight" in a top journal in the field, this research improved the performance of the popular Hadoop platform by over 40%. This work has impacted subsequent in-memory processing research worldwide, such as adaptive task tuning developed by NUCC in USA (Cheng, TPDS 2017), SSD-empowered acceleration by Tsinghua in China (Wang, TBD 2018), and an approach combining HPC and big data paradigm by UMadrid in Spain (Caíno-Lores, FGCS 2020). It has also led to further collaborative research with Worldwide Byte Security Information, Jinan, China (contact: Mr Yuhua Cui, CEO, cuiyh@jzxtsec.com).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -