Dynamically forecasting network performance using the Network Weather Service
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Experiments on the dotresearch.org network and 40 gigabit/s NSF TeraGrid network datasets show that the NN outperforms the NWS bandwidth forecasting models with an error rate of up to 8 and 121.9% for MLP-NN and NWS, respectively.
“40 gigabit/s NSF TeraGrid network datasets show that the NN outperforms the NWS bandwidth forecasting models with an error rate of up to 8 and 121.9% for MLP-NN and NWS, respectively.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [480]
Eswaradass et al. propose a MLP-NN-based bandwidth prediction system for Grid environments and compare it to the Network Weather Service (NWS) bandwidth forecasting AR models for traffic monitoring and measurement.
“[141] propose a MLP-NN-based bandwidth prediction system for Grid environments and compare it to the Network Weather Service (NWS) [480] bandwidth forecasting AR models for traffic monitoring and measurement.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [480]
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Boutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., & Caicedo, O. M. (2018) · Journal of Internet Services and Applications · doi:10.1186/s13174-018-0087-2 · Cited by 1,036 · see citing works
- Authors
- Wolski, R.
- Affiliations
- University of California San Diego (US)
- Corresponding author
- Wolski, Rich
- ORCID
- Wolski, Rich
- Venue
- Cluster Computing
- Published
- 1998-05
- Volume
- 1
- Issue
- 1
- Pages
- 119-132
- ISSN
- 1386-7857 (ISSN-L)
- 1573-7543
- OpenAlex
- W1489252188
- Cited by
- 395
- References
- 27
- Citations by year
- 2026: 1 · 2023: 2 · 2022: 2 · 2021: 1 · 2020: 2 · 2019: 2 · 2018: 2 · 2017: 2 · 2016: 7 · 2015: 7 · 2014: 6 · 2013: 12 · 2012: 9
- FWCI
- 23.81
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Cluster Computing (publishedVersion)
- Funders
- National Centre for Supercomputing Applications
- University of Oregon
- Advanced Research Projects Agency
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed and Parallel Computing Systems
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Traffic and Congestion Control
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Keywords
- metacomputing, quality of service guarantees, job scheduling, dynamic resource allocation
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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