## 5 Conclusions  
This paper proposes a network framework that separates data channels from the existing framework to transmit interaction data between performers and audiences in virtual concerts based on the metaverse platform. In order to provide an immersive experience to the audience, we designed a network using XDN (experience delivery network)-based microservices. Additionally, we presented a task that can visually experience interactions by zone, enabling performers to efficiently receive interactions from a large audience and immerse themselves in virtual concert world. This is done by constructing zones based on the network packet information of the audience and the coordinates in the virtual concert world. In this process, we performed clustering using the fuzzy c-means algorithm to divide the zones according to the varying number of audiences. To reveal the relationship  
Figure 11: Example of the FCM clustering result with clusters represented by different colors. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Clustering Time (ms)**} \\ \hline  & \multicolumn{5}{c}{**Fuzzifier**} \\ \hline  & & 1.1 & 1.2 & 1.3 & 1.4 & 1.5 \\ \cline{2-7}  & 4 & 28.01–217 & 24.38–193 & 25–191.56 & 26–189 & 24–193 \\ \cline{2-7}  & 5 & 29–239 & 29–247 & 30–243 & 29–243.1 & 29–244 \\ \cline{2-7}
**Cluster** & 6 & 35–290 & 34–293 & 34.96–311 & 43.31–299 & 35–314 \\ \cline{2-7}
**Number** & 7 & 41–364.47 & 40–342.41 & 40–350 & 40–340 & 43–338 \\ \cline{2-7}  & 8 & 49–398 & 47–387 & 46–386 & 47–394 & 49–400 \\ \cline{2-7}  & 9 & 51–444 & 52–445 & 57–437 & 55–438.56 & 52–437 \\ \cline{2-7}  & 10 & 58–502 & 59–492.22 & 64–499 & 57–498 & 57–497 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experiment result of FCM clustering. between the hyperparameters of FCM clustering and the clustering performance by generating virtual audiences, we conducted simulation experiments as in this paper. As a result, we derived a latency of about 267-549 ms for matching the audience information to a zone (cluster) and transmitting it to a performer within the network framework. Furthermore, we established the framework of network design at the SDN (software-defined networking) level and proposed an architecture that can communicate interaction data of virtual concerts in each plane. Additionally, to efficiently transmit interaction data from the performer to a large audience, we designed the network by setting up CDN-based edge servers. This research is significant in that it precisely designs a network framework dedicated to interactions, which can be incorporated into the network architecture based on the metaverse platform proposed for virtual concerts. Existing studies have operated virtual concerts using game engines that support a single network or metaverse network architectures that provide various services in an integrated manner. However, by establishing a network architecture specialized for the concert industry as a separate category, we approached dealing with large audiences from a network perspective and proposed a solution for it. In order to transmit and receive interaction data between the audience and the performer in a virtual concert, we examined the existing cloud-based network architecture for virtual concerts and designed it in an SDN-based network architecture so that it could be integrated into the system. As future research, we plan to conduct simulation experiments to enhance the network architecture for transmitting interaction data in a virtual concert architecture configured in a cloud environment. We will assume the total number of audiences for the simulation, configure edge servers in the form of CDN (content delivery network) accordingly, and design a new algorithm capable of handling scalable multi-interaction data by programming the microservices of XDN (experience delivery network). In this process, we will design a task where the performer selects a zone and sends an interaction only to that zone.