Abstract
This paper proposes an adaptive reinforcement co-learning method for solving congestion control problems on high-speed networks. Conventional congestion control scheme regulates source rate by monitoring queue length restricted to a predefined threshold. However, the difficulty of obtaining complete statistics on input traffic to a network. As a result, it is not easy to accurately determine the effective thresholds for high-speed networks. We proposed a simple and robust Co-learning Multi-agent Congestion Controller (CMCC), which consists of two subsystems: a long-term policy evaluator and a short-term rate selector incorporated with a co-learning reinforcement signal to solve the problem. The well-trained controllers can adaptively take correct actions to regulate source flow under time-varying environments. Simulation results showed the proposed approach can promote the system utilization and decrease packet losses simultaneously.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Gevros, P., Crowcoft, J., Kirstein, P., Bhatti, S.: Congestion Control Mechanisms and the Best Effort Service Model. IEEE Network, 16–26 (2001)
Chiu, D., Jain, R.: Analysis of the Increase and Decrease Algorithm for Congestion Avoidance in Computer Networks. Computer Networks and ISDN Systems, 1–14 (1989)
Lee, S.J., Hou, C.L.: A Neural-Fuzzy System for Congestion Control in ATM Networks. IEEE Trans. on System, Man. and Cybernetics 30, 2–9 (2000)
Cheng, R., Chang, C., Lin, L.: A QoS-Provisioning Neural Fuzzy Connection Admission Controller for Multimedia High-Speed Networks. IEEE/ACM Trans. on Networking 7, 111–121 (1999)
Sun, R., Littman, M.L.: Value-function Reinforcement Learning in Markov Games. Journal of Cognitive Systems Research 2, 55–66 (2001)
Hwang, K., Lin, C.: Smooth Trajectory Tracking of Three-Link Robot: A self-Organizing CMAC Approach. IEEE Trans. on Systems, Man and Cybernetics 28, 680–692 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hwang, K., Hsiao, M., Wu, C., Tan, S. (2005). Multi-agent Congestion Control for High-Speed Networks Using Reinforcement Co-learning. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_61
Download citation
DOI: https://doi.org/10.1007/11427469_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25914-5
Online ISBN: 978-3-540-32069-2
eBook Packages: Computer ScienceComputer Science (R0)