## 5 Performance Evaluation
### Performance of Synergetic Software-defined Sampling Approach  
The long flow detection result is shown in Figure 6. Figure 6a is the result of switch A1 and Figure 6b is switch A5. Although both switches undetect some long flows, the controller can still further reduce the missed detection ratio based on switches sampling information and the long flow detection results. The circled flow in Figure 6c is the flow that have been undetectable. In traditional detection schemes, the cost of these streams is detected to increase the sampling rate. In Sonum, they are discovered because the exchange of information between switches, rather than the increase in the sampling rate. Sonum reduces the consumption of hardware resources.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Core Switches Number & 4 \\ Aggregation Switches Number & 8 \\ Edge Switches Number & 8 \\ Server Number & 16 \\ Max Packet Size & 1500 Bytes \\ Switches Interface Rate & 1 Gbps \\ Short Flow Size & 1 KB–100 KB \\ Long Flow Size & 100 KB–2000 MB \\ Long Flow Size & 100 KB–2000 MB \\ Short Flow Number:Long Flow Number & 1:9 \\ Simulation Time & 20 min \\ Initial Sampling Rate & 1000:1, 10,000:1, 100,000:1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation Parameters.  
We compare the missed detection ratio of Sonum with FlowMon [26] in Figure 7. FlowMon is a sampling method based on SDN. The results show that both have achieved better sampling results at higher sampling rate. The missed detection rate of Sonum reduced by 2.3%-5.1% compared with FlowMon.  
As shown in Figures 8 and 9, we compare the susceptibility of three different solutions to long flow detection: Regular sampling, Sonum (no RAA) and Sonum. We use the average number of packets processed until determining long flow in the network as the measure result of sensitivity. Sonum significantly reduces the waiting time required to detect long flow.  
Figure 8: Comparison result of missed detection ratio.  
Figure 6: Long flow detection result of top 1000 flows. (**a**) Switch-A1. (**b**) Switch-A5. (**c**) Controller.  
Figure 7: Comparison of Sonum with FlowMon.  
Figure 10 shows the Sonum sampling performance when \(\lambda=1\) and \(\lambda=0.6\). \(\lambda=1\) means there is no smoothing. After EWMA smoothing (\(\lambda=0.6\)), the sampling rate adjustment becomes more stable. This operation work better in high-speed situations where the flow rate change frequently, as shown in Figure 10c.  
Figure 11 shows the storage requirements of Sonum. The primary factor related to the queue length is the sampling rate. The larger the sampling rate, the longer the queue length. This is because the large sampling rate causes some short flows to be sampled and occupy the buffer queue for a long time. In addition, the sampling rate is larger, the sampling accuracy is higher, and the number of long flows in the storage queue is also larger, which is another reason. The controller has a longer cache queue than the switches because the controller needs to process the long flow information reported from switches. The storage overhead that the controller needs to maintain is greater. Our experiment only counts the deployment of Sonum between switch A1 and switch A5. If all the switches in the data center network need to deploy Sonum, the length of storage queue that the controller needs to maintain will be extremely longer. However, the length of queue is not longer than the number of all long flows in the entire network.  
Figure 12 shows the queue length fluctuation of switch A1. Due to the addition of the life cycle management mechanism, the queue length change can be stabilized within an acceptable range.  
Figure 10: Statistic results of sampling interval when different \(\lambda\). (**a**) sampling rate = 1000:1. (**b**) sampling rate = 10,000:1. (**c**) sampling rate = 100,000:1.  
Figure 9: Comparison result of number of packages.