## 5 Conclusions  
Considering the current trends in networking, a let-up in data expansion is unlikely. On the contrary, the changes in the traffic patterns will be amplified. To adequately support the demand and scale of the continuously increasing workloads, as well as business flexibility and agility, heavy-hitter traffic flow classification will continue to play a key role. However, as we discussed in the previous sections, HH detection in current SDDCN environment is still challenging, especially concerning traffic flow statistics collection and threshold estimation. Aiming at overcoming such challenges, we proposed a novel HHs identification approach based on the KDN concept. This concept takes advantage of ML techniques in SDN. In particular, this paper, firstly, presents a clear understanding of KDN concept. Secondly, it introduces the approach proposed for Heavy-Hitters identification as well as details and evaluates its modules. In the Heavy-Hitters Data Analyser module, we performed a cluster analysis using K-means for clustering the flows. We employed Silhouette analysis to determine the optimal number of clusters. Based on the obtained results, there is no single consistent threshold that separates flows into HHs and non-HHs. The flow sizes have a diverse character that leads to more than two natural clusters. We stress that threshold selection must include a detailed analysis of the network and its kind of traffic. In the Heavy-Hitters Application module, we present MiceDCER, an algorithm that efficiently routes non-HHs by assigning internal PMAC addresses to the edge switches and hosts. Our evaluation reveals MiceDCER significantly reduces the number of rules installed in switches and, therefore, contributes to reducing the delay in SDDCN. To sum up, the per-module evaluation results corroborated the usefulness and feasibility of our approach for identifying HHs.  
As future work, we intend to perform non-threshold-based HHs identification. In this sense, we plan to offer a solution based on a per-flow packet size distribution for predicting in an early flow stage if it will be a HH.  
The authors contributed equally to this manuscript.  
The authors would like to thank the Universidad del Cauca, Fundacion Universitaria de Popayan, and Universidad del Quindio.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(p\)} & \multirow{2}{*}{
\begin{tabular}{c} **Num. of** \\ **Host** \\ \end{tabular} } & \multicolumn{3}{c}{**MiceDCER**} & \multicolumn{3}{c}{**IP-Based**} & \multicolumn{3}{c}{**MAC-Based**} \\ \cline{3-10}  & & **ES** & **AS** & **CS** & **ES** & **AS** & **CS** & **ES** & **AS** & **CS** \\ \hline
16 & 1024 & 38 & 13 & 19 & 53 & 19 & 18 & 1365 & 512 & 512 \\
20 & 2000 & 48 & 15 & 23 & 55 & 23 & 22 & 2666 & 1000 & 1000 \\
24 & 3456 & 54 & 17 & 27 & 57 & 27 & 26 & 4608 & 1728 & 1728 \\
28 & 5488 & 62 & 19 & 31 & 58 & 31 & 30 & 7317 & 2744 & 2744 \\
32 & 8192 & 70 & 21 & 35 & 61 & 35 & 34 & 10,922 & 4096 & 4096 \\
36 & 11,664 & 78 & 23 & 39 & 63 & 39 & 38 & 15,552 & 5832 & 5832 \\
40 & 16,000 & 86 & 25 & 43 & 65 & 43 & 42 & 21,333 & 8000 & 8000 \\
48 & 27,648 & 102 & 29 & 51 & 69 & 51 & 50 & 36,864 & 13,824 & 13,824 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Number of rules generated by MiceDCER, IP-based and MAC -based routing Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.