## 2 Related Works
VPSA uses the previous and current link bandwidth usage data collected by the controller to calculate a suitable path. The arrival of video flows is rejected or accepted based existence of a suitable path. Similarly, [PERSON] et al. [10] leverage the concept of monitoring to pull the statistical data and calculate the port load. Flows are routed through a less loaded path. However, frequent polling of previous and latest link bandwidth usage or port in [10; 16] may significantly affect topology learning and discovering time, which in turn increases the path setup latency [7]. The flow setup latency was measured by ([PERSON] et al., 2018). The author used a special packet issued by the controller to switches to measure the round trip time of packets back to the controller. This way the time is appended to each path to obtain the total path setup latency. [PERSON] et al. [30] extend in the work in [14] considering distributed controllers. However, both schemes may experience higher flow table operation time thereby affecting path setup switching time. An effective method to minimize the total setup switching time of all paths in the network was presented in [PERSON] et al. [31; 32]. The authors considered path route selection based on path switching latency in heterogeneous networks, where switches had different specifications. This way, several paths are explored and the path with the set of switches having the shortest processing time is considered. However, the solution may not give the optimal performance in a large-scale network. Thus, [PERSON] and [PERSON] [33] investigate different objective functions for the optimization model with respect to their impact on network performance such as link utilization and latency. This way path routing was devised based on certain constraints to meet the demand of the network performance. Again, Integer Linear Programming (ILP) model may slow down network convergence of large-scale networks [9]. \begin{table}
\begin{tabular}{p{12.3 pt} p{12.3 pt} p{12.3 pt} p{12.3 pt} p{12.3 pt} p{12.3 pt}} \hline \hline
**Schemes** & **Aim** & **Algorithm/ Scheme** & **Link Latency/ Delay** & **Switch Update Operation** & **Limitation** \\ \hline \multirow{3}{*}{([PERSON] et al. [24; 25])} & To minimized rule update operation & Optimization model minimizes operation on the path & X & ✓ & An optimization scheme may take time to converge in large-scale networks \\ \hline \multirow{3}{*}{[PERSON] et al. [9; 12; 34]} & To devise a path selection scheme with a minimum number of entries & Reliable pathfinder and path selection criteria & X & ✓ & Overlook critical switches, which may affect path setup time \\ \hline \multirow{3}{*}{[PERSON] et al. [26]} & To optimized data transmission through the optimal path & Deep neural network & X & ✓ & Overlook quality of links during route selection decision \\ \hline \multirow{3}{*}{[PERSON] et al. [14]} & To reduce latency and the variance of the flow setup & Aggregated flow setup mechanism & ✓ & X & Path switching time and number of flow rules may affect path setup convergence time \\ \hline \multirow{3}{*}{[PERSON] et al. [15]} & To joint QoS-specific factors to select an optimal path for data delivery & Path selection model & ✓ & & Increased packet processing time due to high number of rules in critical switches \\ \hline \multirow{3}{*}{[PERSON] et al. [27]} & Routing time-sensitive flows based on QoS requirement & QoS-aware path selection algorithm & ✓ & X & Considering the link latency only can’t guarantee the quality of a link \\ \hline \multirow{3}{*}{[PERSON] et al. [29]} & Path selection to maximize the overall network performance & ILP multi-constraint QoS aware routing & ✓ & X & Slow convergence in large scale networks due to frequent changes in network \\ \cline{1-1} \cline{2-6}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the related works. From Table 1, none of the existing works have considered both link quality and switch update operations.