TY  - EJOU
AU  - Kumar, Aashish 
AU  - Anand, Darpan 
AU  - Jha, Sudan 
AU  - Joshi, Gyanendra Prasad 
AU  - Cho, Woong 

TI  - Optimized Load Balancing Technique for Software Defined Network
T2  - Computers, Materials \& Continua

PY  - 2022
VL  - 72
IS  - 1
SN  - 1546-2226

AB  - Software-defined networking is one of the progressive and prominent innovations in Information and Communications Technology. It mitigates the issues that our conventional network was experiencing. However, traffic data generated by various applications is increasing day by day. In addition, as an organization's digital transformation is accelerated, the amount of information to be processed inside the organization has increased explosively. It might be possible that a Software-Defined Network becomes a bottleneck and unavailable. Various models have been proposed in the literature to balance the load. However, most of the works consider only limited parameters and do not consider controller and transmission media loads. These loads also contribute to decreasing the performance of Software-Defined Networks. This work illustrates how a software-defined network can tackle the load at its software layer and give excellent results to distribute the load. We proposed a deep learning-dependent convolutional neural network-based load balancing technique to handle a software-defined network load. The simulation results show that the proposed model requires fewer resources as compared to existing machine learning-based load balancing techniques.
KW  - SDN; software-defined networks; load balancing; performance enhancement

DO  - 10.32604/cmc.2022.024970