# A Review of the Control Plane Scalability Approaches in Software Defined Networking
## 4 Discussion and Possible Future Directions
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Controller Scalability Approach** & **Reference** \\ \hline Centralized Topology & Ethane & [10] \\  & NOX & [11] \\ \hline  & Hyperflow & [21] \\  & Onix & [22] \\  & ElastiCon & [12] \\  & DISCO & [13] \\  & ONOS & [14] \\ \hline  & Kandoo & [15] \\  & Logical xBar & [16] \\  & Orion & [17] \\  & FlowBroker & [18] \\ \hline  & DIFANE & [32] \\  & DevoFlow & [19] \\  & Fibbing & [20] \\ \hline  & NOX-MT & [23] \\  & Beacon & [24] \\  & Maestro & [25] \\ \hline  & RuleTailor & [26] \\  & Agg-ExTable & [27] \\ \hline  & \multicolumn{1}{c}{} & \\ \end{tabular}
\begin{tabular}{c c c} \hline \hline
**Controller Scalability Approach** & **Flow Setup** & **Throughput** & **Category** \\ \hline Ethane & Avg 2.7 ms & Up to 11 K & Centralized topology \\  & NOX & Avg 49 ms & Up to 30 K & \\ \hline ElastiCon & Avg 2.3 ms & Up to 30 K & \\  & Onix & Avg 3.5 ms & Up to 200 K & Distributed (Flat topology) \\  & ONOS & Avg 34 ms & Up to 19 K & \\ \hline  & Kandoo & - & Up to 1.3 M & Distributed (Hierarchical topology) \\  & Orion & Avg 15.3 ms & Up to 50 K & \\ \hline  & DIFANE & Avg 0.9 ms & Up to 3 M & Distributed (Hybrid topology) \\ \hline  & Beacon & Avg 24.7 μs & Up to 12.8 M & \\  & NOX-MT & Avg 2 ms & Up to 1.8 M & Parallelism \\  & Maestro & Avg 55 ms & Up to 3.5 M & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance Comparison of the Control Plane Scalability Approaches. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Controller Scalability Approach** & **Flow Setup** & **Throughput** & **Category** \\ \hline Ethane & Avg 2.7 ms & Up to 11 K & Centralized topology \\  & NOX & Avg 49 ms & Up to 30 K & \\ \hline ElastiCon & Avg 2.3 ms & Up to 30 K & \\  & Onix & Avg 3.5 ms & Up to 200 K & Distributed (Flat topology) \\  & ONOS & Avg 34 ms & Up to 19 K & \\ \hline  & Kandoo & - & Up to 1.3 M & Distributed (Hierarchical topology) \\  & Orion & Avg 15.3 ms & Up to 50 K & \\ \hline  & DIFANE & Avg 0.9 ms & Up to 3 M & Distributed (Hybrid topology) \\ \hline  & Beacon & Avg 24.7 μs & Up to 12.8 M & \\  & NOX-MT & Avg 2 ms & Up to 1.8 M & Parallelism \\  & Maestro & Avg 55 ms & Up to 3.5 M & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the Control Plane Scalability Approaches. learning displayed a very high potential regarding traffic prediction, using real-time data, efficiently organizing flow tables and reducing overhead within the network. For the future research, there are several SDN control plane research challenges along with existing proposals including:  
1. Machine learning will be gathering more attention due to its potential to be used to improve upon already existing proposals. However, existing proposals such as those in References [30; 31; 36] can still be improved by combining some techniques that have been researched up to this date and incorporating them into a new solution. 2. As controller placement [1; 37; 38] has a significant impact on the overall performance (i.e., delays, fault tolerance, resiliency, etc. ), it is considered one of the challenge that worth more investigations. 3. Flow Rule Setup Latency [39; 40] in the context of control plane scalability is a challenge that still needs further attention of researchers.