Flow-Aware Elephant Flow Detection for Software-Defined Networks
- Submitting institution
-
University of Northumbria at Newcastle
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 28029905
- Type
- D - Journal article
- DOI
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10.1109/ACCESS.2020.2987977
- Title of journal
- IEEE Access
- Article number
- 9066961
- First page
- 72585
- Volume
- 8
- Issue
- -
- ISSN
- 2169-3536
- Open access status
- Compliant
- Month of publication
- April
- Year of publication
- 2020
- 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)
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A - Digital Health and Wellbeing (DH&W)
- Citation count
- 3
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This presents a novel approach for detecting elephant flows at both infrastructure and controller layers of software-defined networks. Although recently published in IEEE access (April 2020) it has already been viewed >660 times on the IEEE website (https://ieeexplore.ieee.org/abstract/document/9066961). It has been cited 3 times including in a paper written by a well-known scientist, Professor Athanasios Vasilakos, Department of Computer Science, Lulea University of Technology, Sweden.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -