PSO-Based Adaptive Controller Load Balancing Approach for Hybrid Cluster Architectures

In high-dynamic networks, nodes are affected by mobility and changes in load, leading to issues such as uneven traffic distribution and sudden traffic spikes. To address the challenges mentioned above, the advantages of software defined networking (SDN), particularly the separation between the control and forwarding planes, are leveraged to design a cluster control architecture based on a globally distributed and locally centralized (GDLC) approach. This approach offers improved scalability and flexibility in managing network resources across clusters. Considering the dynamic network environment, we propose a network model and objective function for controller load balancing, aiming to optimize load distribution and minimize network congestion. By analyzing the load distribution of each controller within the cluster, a particle swarm optimization (PSO)-based load-balancing algorithm is proposed, which controls the merging/splitting decisions and switch assignment decisions between controllers, thereby addressing the load balancing issues of the controller cluster. Experimental results show that the proposed algorithm achieves better load balancing and lower migration costs compared to existing methods.