A New Active Queue Management Algorithm Based on Self-Adaptive Fuzzy Neural-Network PID Controller
Abstract
Active queue management (AQM) is a very important research area in congestion control. But the complexity and dynamic characteristic of the computer network cause the traditional PID control algorithm low adaptability to dynamic environment due to its fixed parameters. In order to overcome such shortcomings, intelligent control theory was introduced to congestion control research, and a new AQM algorithm called FAPIDNN was proposed. Fuzzy controller automatically computers the learning rate according to the current network state, and the neural network PID controller calculate the packet dropping probability based on the learning rate provided by the fuzzy controller. Simulation results show that FAPIDNN algorithm is superior to the presented PID controller on the queue stability, convergence speed and time delay.
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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 [485]
Cites
On designing improved controllers for AQM routers supporting TCP flows
Hollot, C. V., Misra, V., Towsley, D., & Gong, W. (2002) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infcom.2001.916670 · Cited by 964 · see citing works
Hayes‐Roth, B. (1993) · Artificial Intelligence · doi:10.1016/0004-3702(93)90188-h · Cited by 399 · see citing works
Design a PID controller for active queue management
Yanfie, F., Fengyuan, R., & Chuang, L. (2004) · Proceedings - IEEE Symposium on Computers and Communications/IEEE Symposium on Computers and Communications · doi:10.1109/iscc.2003.1214244 · Cited by 86 · see citing works
A new PID controller for AQM based on neural network
Yan, Q., & Xiaojuan, H. (2010) · doi:10.1109/icicisys.2010.5658798 · Cited by 6 · see citing works
Self-citationA novel intelligent PID controller for AQM based on neural networks
Zhou, C., Zhang, L., & Qinwei, C. (2008) · Chinese Control Conference (CCC) · doi:10.1109/chicc.2008.4605590 · Cited by 3 · see citing works
Neuro Fuzzy Model Predictive Control of AQM Networks Supporting TCP Flows
Maghsoudlou, A. R., Barzamini, R., Soleimanpour, S., & Jouzdani, J. (2008) · ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) · doi:10.1109/snpd.2008.64 · Cited by 2 · 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
Active Queue Management: A Survey
Adams, R. V. (2012) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2012.082212.00018 · Cited by 283 · see citing works
When machine learning meets congestion control: A survey and comparison
Jiang, H., Li, Q., Jiang, Y., Shen, G., Sinnott, R., Tian, C., & Xu, M. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.108033 · Cited by 107 · 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
A self-tuning controller for queuing delay regulation in TCP/AQM networks
Kahe, G., & Jahangir, A. H. (2018) · Telecommunication Systems · doi:10.1007/s11235-018-0526-1 · Cited by 29 · see citing works
Controlling Delay at the Router Buffer using Modified Random Early Detection
Abu-Shareha, A. A. (2019) · International journal of Computer Networks & Communications · doi:10.5121/ijcnc.2019.11604 · Cited by 21 · see citing works
Swarm intelligence based robust active queue management design for congestion control in TCP network
Ali, H. I., & Khalid, K. S. (2016) · IEEJ Transactions on Electrical and Electronic Engineering · doi:10.1002/tee.22220 · Cited by 15 · see citing works
Berbek, M. I., & Oglah, A. A. (2022) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v26.i1.pp229-242 · Cited by 14 · see citing works
An AQM Controller Based on Feed-Forward Neural Networks for Stable Internet
Bisoy, S. K., & Pattnaik, P. K. (2017) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-017-2767-9 · Cited by 14 · see citing works
Design of an active queue management technique based on neural networks for congestion control
Bisoy, S. K., Pandey, P. K., & Pati, B. (2017) · IEEE International Conference on Advanced Networks and Telecommunications Systems · doi:10.1109/ants.2017.8384104 · Cited by 12 · see citing works
A Survey of Applying Reinforcement Learning Techniques to Multicast Routing
Ashour, O., St‐Hilaire, M., Kunz, T., & Wang, M. (2019) · IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) · doi:10.1109/uemcon47517.2019.8993014 · Cited by 9 · see citing works
Network congestion control methods and theory
Wu, W. H., Du, W., & Ruan, G. (2015) · International Journal of Grid and Utility Computing · doi:10.1504/ijguc.2015.070682 · Cited by 6 · see citing works
Machine learning controller for data rate management in science DMZ networks
Caicedo, C. V., Kfoury, E. F., Gomez, J., Pezoa, J. E. E., Figueroa, M. E., & Crichigno, J. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110237 · Cited by 5 · see citing works
Masood, M. S., & Khader, P. S. A. (2016) · Springer eBooks · doi:10.1007/978-981-10-0287-8_36 · Cited by 2 · see citing works
Fuzzy Like Pid Controller Based on Sso Design for Congestion Avoidance in Internet Router
Berbek, M. I., & Oglah, A. A. (2021) · Iraqi Journal of Computer Communication Control and System Engineering · doi:10.33103/uot.ijccce.21.2.7 · Cited by 2 · see citing works
Figueroa, M., & Pezoa, J. E. E. (2023) · doi:10.29393/tdudec-15vc1ef15 · Cited by 1 · see citing works
Glisic, S., & Lorenzo, B. (2022) · Wiley eBooks · doi:10.1002/9781119790327.ch7
Network traffic control method of NHP based on deep reinforcement learning
Huang, Q., Tan, Z., Wang, Q., Jia, Z., & Chen, B. (2025) · Scientific Reports · doi:10.1038/s41598-025-31556-3
- Authors
- Yan, Q., & Lei, Q.
- Affiliations
- Shenzhen University (CN)
- ORCID
- Yan, Qiao
- Venue
- 2011 International Conference on Internet Technology and Applications
- Publisher
- IEEE
- Published
- 2011-08
- Pages
- 1-4
- OpenAlex
- W2073230557
- Cited by
- 18
- References
- 6
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 1 · 2022: 2 · 2021: 2 · 2019: 2 · 2018: 2 · 2017: 2 · 2016: 2 · 2015: 1 · 2014: 1 · 2012: 1
- FWCI
- 0.57
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Internet Technology and Applications (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, self-adaptive control, packet dropping probability, queue stability, adaptive learning rate
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