A neural-fuzzy system for congestion control in ATM networks
Abstract
We propose the use of a neural-fuzzy scheme for rate-based feedback congestion control in asynchronous transfer mode (ATM) networks. Available bit rate (ABR) traffic is not guaranteed quality of service (QoS) in the setup connection, and it can dynamically share the available bandwidth. Therefore, congestion can be controlled by regulating the source rate, to a certain degree, according to the current traffic flow. Traditional methods perform congestion control by monitoring the queue length. The source rate is decreased by a fixed rate when the queue length is greater than a prespecified threshold. However, it is difficult to get a suitable rate according to the degree of traffic congestion. We employ a neural-fuzzy mechanism to control the source rate. Through learning, membership values can be generated and cell loss can be predicted from the status of the queue length. Then, an explicit rate is calculated and the source rate is controlled appropriately. Simulation results have shown that our method is effective compared with traditional methods.
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The first ML-based approaches for congestion control were proposed in the context of ATM networks.
“the first ML-based approaches for congestion control were proposed in the context of asynchronous t ransfer mode (ATM) networks [175, 264, 284, 437].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [264]
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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
- Lee, S.-J., & Hou, C.-L.
- Affiliations
- National Sun Yat-sen University (TW)
- ORCID
- Shie-Jue Lee
- Venue
- IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics)
- Published
- 2000
- Volume
- 30
- Issue
- 1
- Pages
- 2-9
- ISSN
- 1083-4419 (ISSN-L)
- OpenAlex
- W1974904239
- Cited by
- 45
- References
- 19
- Citations by year
- 2022: 1 · 2021: 1 · 2018: 2 · 2017: 1 · 2015: 1 · 2014: 4 · 2013: 1
- FWCI
- 1.53
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) (publishedVersion)
- PubMed (publishedVersion)
- 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 › Engineering › Control and Systems Engineering › Traffic control and management
- Keywords
- congestion control, ATM networks, fuzzy neural network, available bit rate traffic, quality of service, queue length, membership function learning
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