## 2 Related Research Analysis
Doing so can identify the most appropriate devices to migrate first, thereby determining an optimal deployment sequence & Optimization & Hybrid IP/SDN \\ \hline \multirow{4}{*}{[11]} & Simultaneous optimization of both traffic matrix measurement (TMM) and traffic engineering (TE) while considering the constraints posed by TCAM capacity and flow aggregation, enabling a substantial improvement in TMM accuracy and TE effectiveness & Optimization & SDN \\ \hline \multirow{4}{*}{[12]} & Development of traffic engineering-aware distributed routing (TEDR) algorithm, which maximizes link utilization comparable to full SDN considering TCAM resource limitation & Optimization & Hybrid SDN \\ \hline \multirow{4}{*}{[13]} & Joint mathematical formulation to solve load balancing challenges in cloud computing; two multi-objective particle swarm optimization (MP) models: distance angle multi-objective particle swarm optimization (DAMP) and angle multi-objective particle swarm optimization (AMP); incorporation of meta-heuristic in the cloud networks management layer & Optimization & SDN, Cloud Computing \\ \hline \multirow{4}{*}{[14]} & Dynamic load balancing (LB) scheme integrating genetic algorithm (GA) and ant colony optimization (ACO); fast global search of GA and efficient search of an optimal solution of ACO are achieved; improvement of the round robin and ACO algorithm with optimal path search rate, round trip time, and packet loss rate & Optimization & SDN \\ \hline \multirow{4}{*}{[15]} & Development and implementation of the energy-aware routing multi-level and mapping problem (EARMLP) algorithm to minimize the overall power consumption; optimal routing strategy that considers system configuration and traffic demand between the data and control planes; establishment of the controller placement problem (CPP) to select the optimal locations and controller numbers & Optimization & SDN-based core networks \\ \hline \multirow{4}{*}{[16]} & Mixed integer programming algorithm designed to optimize the network devices’ power consumption utilizing energy-aware traffic engineering & Optimization & SDN Data Centers \\ \hline \multirow{4}{*}{[17]} & Load-balanced and fast failure recovery solution to provision routing paths so a Fibbing network can apply loop-free alternate (LFA) in the network in a case of a single node or single link failure & Optimization & SDN \\ \hline \multirow{4}{*}{[18]} & Multiple distributed controller load balancing (MDCLB) algorithm on an immense-scale SDN-IoT for smart cities & Optimization & IoT, SDN, Smart City \\ \hline \multirow{4}{*}{[19]} & Task allocation in the mobile edge computing (MEC) scenario of ultra-dense network based on routing between MEC servers; load balancing algorithm & Optimization, Genetic Algorithm & SDN, Edge Computing \\ \cline{1-1}  & Joint optimization algorithm for enhancing the performance and traffic load balancing of the wireless network; the objectives are data transmission latency, the energy consumption of wireless microbase stations, and links’ throughput & Optimization, Heuristic Scheme & Wireless Network \\ \hline \multirow{4}{*}{[20]} & Traffic Engineering Fast ReRoute with support of Traffic Policing & \multirow{4}{*}{[21]} & Traffic Engineering Fast ReRoute with support of Traffic Policing \\  & (TE-FRR-TP); optimality criterion focuses on minimizing the dynamically controlled upper bound of links utilization and the intensity of flows that receive a denial of service at the network edge weighted with the priority of serving & Optimization & SD-WAN \\ \hline \multirow{4}{*}{[23]} & Load balancing mechanism based on SDWSN (Software-Defined Wireless Sensor Network); load-balanced routing improvement based on Elman neural network—SDSNLB (Software-Defined Sensor Network Load Balancing) routing algorithm & Optimization, Elman neural network & SDWSN, Smart City \\ \hline \multirow{4}{*}{[24]} & Optimization model with mobile edges for multimedia sensors using artificial intelligence of things (AIoT), which aims to maintain real-time data collection with low resource consumption & Optimization, AI & SDN, AIoT, MIoT \\ \hline \multirow{4}{*}{[25]} & Development of the deep reinforcement learning (DRL)-based TE scheme of multipath transmission to dynamically adjust the traffic splitting ratio among different paths based on the network traffic distribution in IP and segment routing (SR) hybrid network & Deep Reinforcement Learning & Hybrid IP/SR network \\ \cline{1-1}  & & & \\ \cline{1-1}  &An analysis of current research regarding TE implementation in different types of networks shows that the most promising approaches are grounded in optimization methods.