# An Adaptive Symmetrical Load Balancing Scheme for Next Generation Wireless Networks
## 2 Related Work  
### Load Balancing in Control Plane  
A software-defined networking (SDN)-enabled WLAN system architecture was created with a two-level flexible load equalization approach [18]. Load equalization functions on the controller side were implemented resulting in scalability at the control plane. The affiliation indexes preserved are detached using a policy of LIFO (Last-In-First-Out) [19; 20]. Consequently, the first consumer in line has to endure a longer wait time than the last user in line. The incoming streams are categorized based on priority, disregarding the sequence in which the data packets have arrived. Research has suggested a collaborative burden equalization plan for hierarchical SDN regulators, where a top-level regulator is designated to manage the flow requests of other regulators [21]. In line with the adjustment in traffic circumstances, the guidelines are correctly established and reinstated to achieve burden equalization. The top-level regulator is accountable for connecting with other typical regulators, allowing for the creation of a flexible burden equalization multi-regulator structure. The scheme ignores the latency factor. Controllers are organized in clusters, and whenever a cluster attains the utmost burden limit, a proportion of the burden is transferred to the controller with lighter workloads [22]. In situations of overcapacity, the clusters are reconfigured. However, the study was unable to devise a method for determining the burden on each controller. The scheme we propose diminishes the waiting time in request processing by prioritizing the data streams. \begin{table}
\begin{tabular}{c c} \hline \hline
**Symbols** & **Full Form** \\ \hline PC & Packet Category \\ \hline L\({}_{t}\) & Limit of Flow \\ \hline \(\eta\) & Load of Regional Controller \\ \hline X\({}_{\eta}\) & Central Processor Usage \\ \hline Y\({}_{\eta}\) & Memory Usage \\ \hline Z\({}_{\eta}\) & Hard Drive Usage \\ \hline Ap & Amount of Packet Processing \\ \hline Pb & Packets in Buffers \\ \hline B & Burden Status \\ \hline S\({}_{\eta}\) & System Parameter \\ \hline C\({}_{\eta}\) & Consumer Parameter \\ \hline CI & Consistency Index \\ \hline \(\lambda\) & Eigenvalue \\ \hline CI/RI & Consistency Ratio \\ \hline RI & Random Index \\ \hline Vol\({}_{\text{d}}\) & Volume of Data of Aps \\ \hline n\({}_{\text{d}}\) & Number of Users AP can Maintain \\ \hline Us\({}_{\text{d}}\) & Users Currently connected to AP \\ \hline pdp\({}_{\text{d}}\) & Packet Distortion Proportion \\ \hline B\({}_{\text{d}}\) & Burden data \\ \hline msir\({}_{\text{d}}\) & Mean Signal Inaccuracy Rate \\ \hline cpc\({}_{\text{d}}\) & Central Processing Consuming \\ \hline mu\({}_{\text{d}}\) & Memory Utilization \\ \hline Q & Utilization Level \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of Symbols. SDN was integrated into the cloud domain to distribute the workload through implementation of the honeybee foraging algorithm using planned installations [23; 24]. The selection process is based on First-In-First-Out, whereas the algorithm is employed for decision-making. The burden at each node is anticipated based on CPU utilization details and the user is allocated accordingly. A Finite State Machine (FSM) and guideline are devised [25]. The FSM was utilized to align the states of controllers. Throughput performance was not evaluated. Load equalization was accomplished through utilization of a genetic algorithm [26]. The GA entails a selection process, crossover and mutation for estimating the optimal load balancing among the controllers. Although these measures enable the detection of the burden on the controllers, they fall short of equalizing the load when additional incoming requests are received. The scheme we propose demonstrates effective processing of data packets, even in the presence of a large number of requests. The round-robin mechanism was one of the prevalent techniques employed for load balancing [27]. The Weighted Round-Robin approach (WRR) was used to distribute the workload in SDN.