MLMBN mechanism optimizes network load balance using information with multiple controllers
International journal of emerging multidisciplinary research
Background/Objectives: In this paper, we propose a Deep Learning Mechanism (DLMBN) mechanism (Deep Learning Mechanism on Blockchain) that optimizes the load balance that can occur in the network by deep learning some important information related to the load balance after connecting the information of multiple distributed controllers into the blockchain. Methods/Statistical analysis: The proposed mechanism binds and manages the load of each controller distributed over the network with a
... in, thus reducing load time while dynamically balancing the load balance. In particular, deep learning technology was used to ensure that each controller classified as a group would not be biased to one side and would maintain a balanced load balance across the entire network. Findings: As a result of the experiment, the proposed mechanism improved the load balance retention time by 14.6% on average compared to the mechanism previously studied, and the efficiency of SDNs processed in multiple groups by 17.3% on average. In addition, the overhead of SDNs for each group was lowered by 7.9%. Improvements/Applications: Based on the results of this study, future studies plan to apply the results to the actual network and test whether the performance analysis results are applicable to heterogeneous networks consisting of heterogeneous devices.