No binding machine learning architecture for SDN controllers

Abstract

Although software-defined networking (SDN) has improved the network management process, but challenges persist in achieving efficient load balancing among distributed controllers. Present architectures often suffer from uneven load distribution, leading to significant performance deterioration. While dynamic binding mechanisms have been explored to address this issue, these mechanisms are complex and introduce a significant latency. This paper proposes SDN_CTRLML , a novel approach that applies machine learning mechanisms to improve load balancing. SDN_CTRLML introduces a scheduling layer that dynamically assigns flow requests to controllers using machine learning scheduling algorithms. Unlike previous approaches, SDN_CTRLML integrates with the standard SDN switches and adapts to different scheduling algorithms, minimizing disruption and network delays. Experimental results show that SDN_CTRLML has outperformed static-binding controllers models without adding complexities of dynamic-binding systems.