Network bandwidth predictor (NBP): a system for online network performance forecasting
Abstract
The applicability of network-based computing depends on the availability of the underlying network bandwidth. However, network resources are shared and the available network bandwidth varies with time. There is no satisfactory solution available for network performance predictions. In this research, we propose, design, and implement the NBP (network bandwidth predictor) for rapid network performance prediction. NBP is a new system that employs a neural network based approach for network bandwidth forecasting. This system is designed to integrate with most advanced technologies. It employs the NWS (network weather service) monitoring subsystem to measure the network traffic, and provides an improved, more accurate performance prediction than that of NWS, especially with applications with a network usage pattern. The NBP system has been tested on real time data collected by NWS monitoring subsystem and on trace files. Experimental results confirm that NBP has an improved prediction.
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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 [141]
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 [141]
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 [141]
Cites
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Self-citationA Neural Network Based Predictive Mechanism for Available Bandwidth
Eswaradass, A., Sun, X., & Wu, M. (2005) · Proceedings - IEEE International Parallel and Distributed Processing Symposium · doi:10.1109/ipdps.2005.51 · Cited by 53 · see citing works
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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
- Eswaradass, A., Sun, X.-H., & Wu, M.
- Affiliations
- Illinois Institute of Technology (US)
- ORCID
- Venue
- Sixth IEEE International Symposium on Cluster Computing and the Grid (CCGRID'06)
- Publisher
- IEEE
- Published
- 2006
- Pages
- 4 pp.-268
- OpenAlex
- W2144996661
- Cited by
- 35
- References
- 5
- Citations by year
- 2023: 3 · 2022: 3 · 2021: 2 · 2020: 3 · 2019: 4 · 2018: 2 · 2017: 2 · 2016: 1 · 2015: 1 · 2014: 2 · 2013: 1 · 2012: 3
- FWCI
- 1.16
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) (publishedVersion)
- Funders
- National Science Foundation
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Traffic and Congestion Control
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed and Parallel Computing Systems
- Keywords
- neural network forecasting, network traffic monitoring
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