# SDN-OpenFlow Topology Discovery: An Overview of Performance Issues
## 3 Recent SDN Topology Discovery Performance Studies
### Flow Table Management Algorithms
In the same dynamic allocation technique, the authors in [91] used dynamic inertia value for short live flows and set the hard timeout value for long live flows with a short time between packets arrival. This has greatly reduced the Packet_In numbers and as a result, reduced SDN-Controller overhead. By utilizing OpenFlow's built-in data collecting, the limitation of LRU is resolved. Flows with a low packet count are victims, so deleting them improves the amount of the limited storage. Without modifying the design of SDN, it is effective to conclude that their proposal reduced network cost and improved the restricted flow table usage to some level. In [92], the authors offered an adaptive flow table alteration based on the fraction of active flow entries by merging the flow table and cost of the SDN-Controller. The algorithm constantly examines traffic, and as a result, the procedure adjusts the idle timeout value based on the flow. Surprisingly, the algorithm was able to set different timeout settings for distinct flows. On the other hand, the process of determining the cost of flow table entries in the cost of the switch and SDN-Controller computing adds additional computational expenses to the SDN-Controller. Another technique that was used to reduce SDN-Controller overhead, the authors in [93] employed a packet filtering scheme. The mechanism started by inspecting the header in Packet-In messages and dropping the duplicate one. Moreover, in [94] the authors devised a blackhole technique to reduce SDN-Controller overhead and preserve the advantage of visibility for each new flow. The packet forwarding architecture between SDN-Switch and SDN-Controller has been changed so that only the first packet is routed to the SDN-Controller and successive table-misses are dealt with locally. In addition, its architectural design leaves it vulnerable to dropping alerted packets, which could result in a substantial number of packet losses. However, the amount of events transmitted to the SDN-Controller is minimized. In addition, another technique based on flow entries aggregation has been proposed in [95] to reduce the load on TCAM. Theoretically, the strategy reduces the number of entries to be saved by compressing fine-grained sending entries into less coarse-grained entries with a somewhat greater matching range. Surprisingly, it is a software application that is easy to install as an additional plug-in on the OpenFlow SDN-Controller and does not require additional hardware. However, since the aggregation methodology fails to preserve the original semantics of the rule in most circumstances, the SDN-Controller has certain issues when changing the direction of entries or querying the traffic stats counter. A different technique based on machine learning has been proposed to identify the classes of traffic flows [96; 97] to predict the duration of the flow entry. In [97], the authors used machine learning techniques to determine which flow should be eliminated. The algorithm, which is based on past data of flow entries, forecasts and with the flow entry with the shortest time being the victim. Depending on the output of the algorithm used, it is determined whether the flow entries are active or inactive. Moreover, in [96], the authors also used the Q-Learning rule for selecting effective timeout values for flows to improve OpenFlow SDN-Switch performance. These techniques, on the other hand, may provide superior performance in a small-medium-sized network, but a large-scale dynamic network may necessitate a more complex training set, which in turn necessitates greater storage to handle more historical data.