# An Enhanced Message Distribution Mechanism for
## 5 Simulation and Evaluation
### The Impact of Subscription Rates  
In our previous experiments, the default redundant app is not interested in packet-in packets at all; this extreme assumption makes our EMD have the best performance. Unfortunately, this is not possible. Ref. [32] points out that, in order to further meet the requirements of function and performance, SDN networks will have various complex combinations of apps running in parallel. In addition, no detailed research on the number and types of SDN apps has been found in the existing literature. Accordingly, in this  
Figure 10: End-to-End Delay Test under Various Forwarding Path Length.  
section, we want to evaluate the performance changes of the EMD under different app subscription rates through simulation.  
It should be noted that the subscription rate mentioned in this experiment refers to the proportion of apps that are interested in a certain topic, among all apps. In the case of multiple topics, the maximum subscription rate of all topics is taken.  
\[r_{sub}=\max_{\forall t\intopic}\frac{n_{t}}{n_{reg}} \tag{8}\]  
where \(r_{sub}\) is the subscription rate; \(n_{t}\) is the number of apps subscribed to the t topic; and, \(n_{reg}\) is the total number of registered apps.  
For example, among ten apps, No.1 to No.3 are interested in match_1, No.5 to No.8 are interested in match_2, and No.8 to No.10 are interested in match_3. In this case, the subscription rate is 40% instead of 90%. We have completed supplementary experiments, the performance in the multi-topic scenario is basically equivalent to that in the single-topic scenario according to the above rules.  
This experiment environment is basically the same as Section 4.1. The test software is bench using both latency mode and throughput mode, and the controller is Ryu. The difference is that some redundant apps also subscribe to messages to implement experimental environments with different subscription rates.  
In Figure 11, the bar graph is the throughput test result and another is the latency test. In the throughput test, we can see that, when the subscription rate is low, the effect of the EMD is extremely good. The effect gradually decreases as the subscription rate increases, but it is still better than the normal model before the subscription rate is 70%. This situation is actually in line with our expectations. When the subscription rate is too high, the classification process of the EMD becomes useless, even a burden, because the message needs to be forwarded to almost all apps.  
Similarly, the latency test also obtains almost the same result. When the subscription rate is about 90% and higher, the single response latency of the EMD is higher than that of the normal mode. In addition, when comparing the two experiments, it can be seen that, when the subscription rate is low, as compared with the throughput, the single response latency deteriorates slightly with the increase of the subscription rate.  
Our original intention in designing EMD is to avoid processing uninteresting messages, so pre-classification is our advantage. When the subscription rate of a topic is too  
Figure 11: Latency And Throughput Test under Various Subscription Rates.  
high, we can consider directly forwarding it without classifying it, which can obtain the maximum benefit.