Support vector regression for link load prediction
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SVR is able to achieve 9000 forecast per sec with 10 input samples, and shows the potential for real-time operation.
“Most importantly, SVR is able to achieve 9000 forecast per sec with 10 input samples, and shows the potential for real-time operation.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [52]
Bermolen et al. consider the prospect of applying SVR for link load forecasting.
“Bermolen et al. [52] consider the prospect of applying SVR for link load forecasting.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [52]
At 1sec timescale, the SVR model shows a slight improvement over an AR model in terms of RMSE, and a more significant 10% improvement over a MA model.
“At 1sec timescale, the SVR model shows a slight improvement over an AR model in terms of RMSE.A more significant 10% improvement is achieved over a MA model.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [52]
TSF approaches, such as [52, 86, 104, 141, 500], where NNs are used to infer traffic volumes from past measured volumes, show high long-term and short-term prediction accuracy at low complexity with limited number of features and limited number of layers and neurons.
“TSF approaches, such as [52, 86, 104, 141, 500], where NNs are used to infer traffic volumes from past measured volumes, show high long-term and short-term prediction accuracy at low complexity with limited number of features and limited number of layers and neurons.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [52]
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Research works printed but not linked: 3
- A.H. Sung, S. Mukkamala, Identifying important features for intrusion detection using SVM and neural networks, Proc. of IEEE SAINT'03, Orlando, FL, USA, 2003.
- I. Mierswa, M. Wurst, R. Klinkenberg, M. Scholz, T. Euler, YALE: rapid prototyping for complex data mining task, Proc. of ACM SIGKDD'06, PA, USA, 2006.
- Dario Rossi was born in Torino, Italy, on a sunny sunday of March 1977. He received his M.Sc. degree in Electronic Engineering in 2001 and his Ph.D. in Electronic and Telecommu- nication Engineering in 2005, both from Politecnico di Torino. Between September 2003 and August 2004 he held a visiting researcher position in the Computer Science division at University of California, Berkeley. Since October 2006, he is an Associate Pro- fessor at the INFRES department at Ecole Nationale Superieure de Telecommunications (ENST), in Paris, France. He has coauthored over 30 papers in leading conferences and journals and currently holds three patents. He partici- pated in the program committees of several conferences including IEEE ICC, IEEE IPCCC and IEEE ISCC. He is responsible for several European research projects, such as FP7 NAPA-WINE, Celtic TIGER and Celtic TRANS. His research interests are in the fields of peer-2-peer networks, Internet traffic measurement, sensor and vehicular networks.
Cited by
Sorted by citations
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
- Bermolen, P., & Rossi, D.
- Affiliations
- Télécom Paris (FR)
- Corresponding author
- Rossi, Dario
- Venue
- Computer Networks
- Publisher
- Elsevier BV
- Published
- 2009-02
- Volume
- 53
- Issue
- 2
- Pages
- 191-201
- ISSN
- 1389-1286 (ISSN-L)
- OpenAlex
- W2170851227
- Cited by
- 121
- References
- 28
- Citations by year
- 2026: 6 · 2025: 6 · 2024: 9 · 2023: 9 · 2022: 11 · 2021: 14 · 2020: 7 · 2019: 8 · 2018: 6 · 2017: 5 · 2016: 3 · 2015: 4 · 2014: 8 · 2013: 1 · 2012: 9
- FWCI
- 8.21
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- cc-by-nc-nd
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Locations
- Computer Networks (publishedVersion)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Traffic and Congestion Control
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Keywords
- support vector regression, network traffic prediction, short-term forecasting, parameter sensitivity analysis, computational complexity, online learning
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