# A Review of the Control Plane Scalability Approaches in Software Defined Networking
## 3 Mechanisms-Related Approaches
### Routing Scheme Based Optimization  
Routing scheme-based optimization is another way to improve control plane scalability. It worth explaining how the OpenFlow protocol deals with flows in SDN. Supposing that a host sends a certain request to the switch, the switch will check its local flow table if the packet matches to an existing flow entry in order to send the packet to its right destination. If the switch does not find the entry in its flow table, it forwards the packet to the control plane. The controller then sends a flow modification message to the switch. Upon receiving the message, the switch stores the received entry in its flow table. This enables the switch to handle the same request in the future without contacting the control plane [33]. The following approaches try to optimize the routing scheme in terms of their flow tables.  
The RuleTailor optimization scheme [26] enables a controller to handle more requests in a certain amount of time through focusing on high speed flow table updates to achieve a higher performance within SDN. RuleTailor applies various methods to alleviate bottlenecks around controllers like as instruction type transformation, pseudo deletion and match field distance. In order to reduce latency in updating the flow table, RuleTailor uses the above mentioned methods together with priority sorting. Furthermore, it reduces the match field distance enabling faster modifications of the flow table. This enables RuleTailor as a TCAM memory based approach to speed up the performance by the factor of 10 in comparison to previous solutions like Tango.  
Another solution is Agg-ExTable [27], which can be classified under the category of routing scheme-based approaches. Since OpenFlow has evolved to the usage of multiple flow tables (MFT), different solutions take advantage of this development. In order to improve the control plane scalability, Agg-ExTable administrates the MFT in a very efficient way. With particular algorithms MFT entries are being regularly aggregated in order to conserve resources, like memory space. Another algorithm attempts to find out frequently used flow entries, so that they can be stored in a frontend ExTable and accessed faster. Therefore, AggExTable is able to save approximately 45% of space in terms of flow tables and represents a good way of resolving the above mentioned issues with MFT.  
When thinking about the strengths of the above approaches, it seems that they are more efficient than the parallelism-based approaches. Certainly, it is an advantage to exploit the power of parallelism in order to handle requests within the control plane faster but it does not reduce the network load in terms of operations. However, to modify the routing-scheme allows you to reduce the network load so that less operations has to be performed in order to achieve the same result. This can be used to increase the scalability of the controller.  
Despite the advantages of Routing Scheme Based Optimization, it faces some issues which limit the usage of the above approaches. All of them are TCAM-based, a certain kind of memory that has high speed flow table lookup and editing process in the switch. TCAM is considered an expensive solution and many research works are devoted to reduce the usage of TCAM in OpenFlow-based network devices. Additionally, Routing Scheme Based Optimization aims to conserve resources in terms of memory space used to manage flow tables efficiently. Algorithms are used in certain periods to reorder lookup tables and hence enabling fast packet matches. However, the limiting factor to increase the potential of the routing scheme based optimization is the uncertainty about the upcoming traffic.