HeavyKeeper: An Accurate Algorithm for Finding Top-$k$ Elephant Flows
- Submitting institution
-
Queen Mary University of London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 528
- Type
- D - Journal article
- DOI
-
10.1109/tnet.2019.2933868
- Title of journal
- IEEE/ACM Transactions on Networking
- Article number
- 5
- First page
- 1845
- Volume
- 27
- Issue
- 5
- ISSN
- 1063-6692
- Open access status
- Deposit exception
- Month of publication
- August
- Year of publication
- 2019
- 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
-
6
- Research group(s)
-
-
- Citation count
- 4
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Finding the largest flows that go through a router in a network is an important task in Internet measurements, but doing it in a scalable way at high speed is challenging. Work introduces a new strategy that balances space requirements and accuracy, using small and constant processing overhead per packet, providing high-precision measurements with small memory size requirements, and low errors compared to the state-of-the-art. Provides scalable measurements for the Alan Turing funded project "Learning-based reactive Internet Engineering" (2019 to 2021), and is currently part of a new EPSRC grant application between QMUL and UCL (result pending @2/12/20).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -