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Adaptive Software-Defined Network Control Using Kernel-Based Reinforcement Learning: An Empirical Study
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DOI：10.3390/app152312349.png)
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Abstract
En 中文
Software-defined networking (SDN) requires adaptive control strategies to handle dynamic traffic conditions and heterogeneous network environments. Reinforcement learning (RL) has emerged as a promising solution, yet deep RL methods often face instability, non-stationarity, and reproducibility challenges that limit practical deployment. To address these issues, a kernel-based RL framework is introduced, embedding transition dynamics into reproducing kernel Hilbert spaces (RKHS) and combining kernel ridge regression with policy iteration. This approach enables stable value estimation, enhanced sample efficiency, and interpretability, making it suitable for large-scale and evolving SDN scenarios. Experimental evaluation demonstrates consistent convergence and robustness under traffic variability, with cumulative rewards exceeding those of baseline deep RL methods by more than 22%. The findings highlight the potential of kernel-embedded RL as a practical and theoretically grounded solution for adaptive SDN management and contribute to the broader development of intelligent systems in complex environments.
Keywords:
software-defined networking
reinforcement learning
kernel methods
reproducing kernel Hilbert space
adaptive network control
non-stationary environments
policy iteration
intelligent systems
Journal
A
IF:
2.5
Papers:
5.9K
Citations:
4