Congestion control mechanism for ATM networks using neural networks
Abstract
This paper presents a new approach to the problem of congestion control arising to the user-to-network interface (UNI) of the ATM-based broadband integrated services digital networks (B-ISDN). Our approach employs an adaptive rate based feedback control algorithm using reinforcement learning neural networks (NNs). The reinforcement learning NN controller provides an adaptive optimal control solution. This is achieved via the formulation of a performance measure function (cost function) that is used to, adaptively, tune the weights of the NN. The cost function is defined in terms of two main objectives: (1) to minimize the cell loss rate (CLR), i.e., control congestion and (2) to preserve the quality of the voice/video traffic via maintaining the original coding rate of the multimedia sources. The results show that the NN control system is adaptive in the sense that it is applicable to any type of multimedia traffic. Also, the control signal is optimal in the sense that it maximizes the performance of the system which is defined in terms of its performance measure function. Hence, our novel approach is very effective in controlling the congestion of multimedia traffic in ATM networks.
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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 [437]
Cites
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Self-citationThe policing function in an ATM network
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Self-citationA novel neural network traffic enforcement mechanism for ATM networks
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- Authors
- Tarraf, A. A., Habib, I. W., & Saadawi, T. N.
- Affiliations
- City College of New York (US)
- Venue
- Proceedings IEEE International Conference on Communications ICC '95
- Publisher
- IEEE
- Published
- 2002-11-19
- Volume
- 1
- Pages
- 206-210
- ISSN
- 1550-3607 (ISSN-L)
- OpenAlex
- W2164131706
- Cited by
- 21
- References
- 8
- Citations by year
- 2022: 1 · 2018: 1 · 2013: 1
- FWCI
- 1.11
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Communications (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Traffic and Congestion Control
- Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- congestion control, ATM networks, B-ISDN, adaptive rate control, cell loss rate, multimedia traffic, cost function
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