An adaptive AQM algorithm based on neuron reinforcement learning
Abstract
In recent years, it has become an active research direction to develop adaptive and robust active queue management (AQM) scheme for congestion control of complex time-varying network. A novel adaptive AQM scheme based on neuron reinforcement learning (NRL) is presented in this paper. This scheme uses queue length and link rate as congestion notification to determine an appropriate drop/mark probability, and the parameters of neuron can be adjusted online according to the time-varying network environment so that the stability of queue dynamics and robustness for fluctuation of TCP loads are guaranteed. This scheme is easy to implement with simple structure, and it is independent of the model of plant to be controlled. Simulation results show that this proposed algorithm is especially suitable for solving the complex network congestion control problem, and also has better stability and robustness.
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These AQM schemes apply different supervised techniques for TSF and reinforcement-based methods for deducing the increment in the packet drop probability.
“these AQM schemes apply different supervised techniques for TSF [160, 179, 212, 498] and reinforcementbased methods for deducing the increment in the packet drop probability [298, 427, 428, 485, 499].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [499]
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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
Li, F., Sun, J., Zukerman, M., Liu, Z., Xu, Q., Chan, S., Chen, G., & Ko, K.-T. (2014) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2014.01.005 · Cited by 33 · see citing works
- Authors
- Zhou, C., Di, D., Chen, Q., & Guo, J.
- Affiliations
- Nanjing University of Science and Technology (CN)
- Venue
- 2009 IEEE International Conference on Control and Automation
- Publisher
- IEEE
- Published
- 2009-12
- Pages
- 1342-1346
- ISSN
- 1948-3449 (ISSN-L)
- OpenAlex
- W2158470215
- Cited by
- 17
- References
- 12
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 2 · 2022: 2 · 2021: 4 · 2020: 1 · 2018: 3 · 2017: 2 · 2014: 1
- FWCI
- 0.00
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Control and Automation (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 › Computer Networks and Communications › Software-Defined Networks and 5G
- Keywords
- active queue management, congestion control, packet dropping probability, congestion notification, queue length, online parameter tuning, queue stability, robustness
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