## 2 Background and the Related Work
### Related Works
Applying four ensemble algorithms--Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM)--researchers examine the classification performance of each algorithm in terms of accuracy, precision, recall, F 1-score, training time, and classification time. The experimental results show that classifiers based on ensemble models outperform those based on the proposed framework and the real-world network traffic dataset. Notably, the classification performance achieved by the LightGBM model is the best. [PERSON] et al. [43] look at existing deep learning (DL) techniques for classifying and predicting traffic in an SDN environment. [PERSON] et al. [44] offer Deep-SDN, a new deep learning model for SDN that can quickly and reliably identify a variety of traffic applications. Better results were reported in terms of accuracy, precision, recall, and f-measure when the proposed model's performance was compared to the state of the art. According to the research, the proposed model could achieve an overall accuracy of 96%. The researcher's claim is that the proposed model can accurately and quickly identify the different network traffic application types, making it useful for identifying online traffic. Load balancing is a technique used to divide the workload among multiple resources in order to avoid overloading any of the resources [2]. Some of the objectives of load balancing include maximizing throughput, minimizing response time, and optimizing traffic. [PERSON] et al. in [45] balance the load with a focus on the controller. The incoming traffic is balanced using virtual SDN (vSDN) controller duplication and shared load. Using two vSDN controllers in a mininet emulator, the authors experimentally confirmed the load balancing in the Fat-Tree architecture. [PERSON] et al. [46] provide load balancing in the control plane based on load status and the dynamic weight coefficient of each controller to improve resource use, reliability, and resilience in the network. Additionally, compared to current methods, communication overhead is greatly decreased by employing a pre-defined load threshold. [PERSON] et al. [47] provide an in-depth review of recently introduced SDN-based load balancing and routing methods. There are different types of possible load balancing techniques, such as switch migration, routing, controller placement, traffic classification, heuristic algorithms, AI-based techniques, etc. [PERSON] et al. [48] propose CORE, a prediction-based technique consisting of mobility prediction, rule-caching, and master controller assignment, to minimize controller overload and distribute the dynamic traffic optimally under the consideration of heterogeneous IoT devices. [PERSON] et al. [49] propose the Naked Mole-Rat (NMR) algorithm, a novel population-based meta-heuristic algorithm, to optimize the location for controller placement based on switch-controller and controller-controller latency while maintaining load balance among the controllers. Two commonly available standard topologies, Ernet and Savvis, are used to demonstrate the concepts and methods. [PERSON] et al. [50] propose a novel approach for load balancing of SDN which combines Genetic-Ant (GA) with Colony Optimization (ACO), called (G-ACO). It benefits from the speedy genetic algorithm (GA) global search and the effective search for an ACO optimal solution. Computer simulation reveals that the proposed scheme significantly improves the round trip time (RTT) and packet delivery ratio compared to the Round Robin (RR) and ACO algorithms by effectively achieving the LB.