## 2 Related Work
& The MCPP was resolved for distribution simulation, and execution time between S2C and C2C was solved for all metrics. \\  & The futuristic view of the researcher is that this work can be expanded by making the position of the controllers and the task of switching energetic. Moreover, controller latency can be included within the objective function and considered as a load-balancing aspect. & Implement a greedy SA algorithm to solve the CPP; the goal was to reduce the distance between all nodes and selected controllers by verifying all metrics. \\ \hline \multirow{8}{*}{[2]} & Handles a limited number of resources within the network, and digital controllers require that all network elements within the network be covered in an optimal manner. The primary criterion was to reduce the distance between all nodes and selected controllers by a greedy SA algorithm. & Implement a greedy SA algorithm to solve the CPP; the goal was to reduce the distance between all nodes and selected controllers by a greedy SA algorithm. & The primary criterion was to reduce the distance between all nodes and selected controllers. & reduce the distance between all nodes and selected controllers. \\ \hline \multirow{8}{*}{[3]} & Focused on the resource mapping model. & Feedback-sensitive resource mapping based on an SA algorithm in an SDN. & SA-MCSDN was used to solve the MCPP. \\ \hline \multirow{8}{*}{[4]} & Used machine learning and focused only on network traffic analysis, traffic prediction, and fault diagnosis. & SA algorithm used for path optimization for network control. & SA-MCSDN was used to solve the MCPP. \\ \hline \multirow{8}{*}{[5]} & Focused only on the flexibility dynamic control plane (DCPP) to reduce the total cost without using metrics for measure scalability. & Flexible design of DCPP in an SDN by an online SA algorithm. & SA-MCSDN was used to solve the MCPP. \\ \hline \multirow{8}{*}{[6]} & SA and QA performance is significantly more scalable with higher synchronization cost. & Multi-objective placement evolution of SDN controllers to improve cost and network performance in WSN. & The SA-MCSDN method was used to solve the MCPP in terms of metrics, performance, and cost. \\ \hline \multirow{8}{*}{[7]} & One approximate comparison method was used (n-k-means vs. n times k-means) running a time clustering algorithm, which increased performance by 17.7\%, whereas the controller optimized load dynamic strategy (COLDS) & Multi-controller deployment of 6G SAGIN in an SDN by an SA algorithm. & The running time of the algorithm (SA-MCSDN) increased by 3.33\%. \\  & \multirow{2}{*}{[8]} & Fourth-generation long-term evolution (4G LTE-Unlicensed) was implemented with LTE-U-CPP-RS ray-shooting heuristic; the simulation results revealed better and more accurate results than the LTE-U-CPP-SA heuristic. & The use of LTE-U-CPP-SA and LTE-UCPP-RS algorithms to solve WCPP based on a 4G LTE network with SBI. & SA-MCSDN was used to solve the MCPP. \\ \hline \multirow{8}{*}{[9]} & Experiments showed that Cuckoo-PC surpasses both SA and QA in terms of a range of performance indicators. & Cuckoo placement of controllers (Cuckoo-PC algorithm) in an SDN, employing SA and QA to optimize the network performance in WSNs. & SA-MCSDN was used to solve the MCPP. \\ \hline \multirow{8}{*}{[10]} & By learning with AHP-SA and SA-SWNO, the execution time can be reduced to optimize the weights of factors. & SA-based multilink choice calculation algorithm in SDN-enabled avionic networks. & Without learning, SA-MCSDN can decrease the execution time and encourage optimization of the components. \\ \hline \multirow{8}{*}{[11]} & Focused on delay, C2C, and S2C from where link failure originated, as well as reliability for CPP. Used two & Dynamic placement of MCSDN and allocation of computational and computational resources based on a heuristic ant colony algorithm. & According to the results of the SA-MCSDN optimized algorithm, it achieved the best performance solving the problem of delay and reliability.