Load balancing strategy for SDN multi-controller clusters based on load prediction

Abstract

Software-defined networking (SDN) separates the control layer from the data layer, and decisions to manage the network are issued through a controller. The distributed SDN architecture is an effective solution addressing modern WAN SDN architectures and allows multiple controllers to manage different parts of the network to ensure efficient and stable operation. To solve the problems of high switch migration cost, load imbalance, and inefficient load balancing in SDN multi-controller environments, we propose a deep learning-based controller load prediction switch migration (LPSM) strategy that uses a migration switch selection algorithm, target controller selection algorithm, and switch migration decision algorithm. Then, we propose a load balancing algorithm based on this decision algorithm. The final experimental results show that the LPSM reduces the migration cost by 16% and 8%, respectively, compared with time-sharing switch migration (TSSM) and distributed decision migration (DDM) strategies, reduces load variance from 0.02 to 0.004 compared with the DDM strategy, and improves load balancing efficiency by 27.6% compared with the TSSM strategy.

GIJA:Enhanced geyser‐inspired Jaya algorithm for task scheduling optimization in cloud computing

“…The related work section discusses various task‐scheduling optimization strategies employed in cloud computing, including traditional optimization algorithms, metaheuristic approaches, and nature‐inspired algorithms 28,29 . It will explore the strengths and limitations of these methods, along with their applicability to real‐world cloud computing scenarios 30,31 . Additionally, this section reviews studies that have investigated the integration of different algorithms or the enhancement of existing algorithms to improve task scheduling efficiency in cloud environments 32,33 …”