## 2 Materials and Methods
### MK-Mediod Algorithm
#### 2.3.3 Collaborative Storage and Resolution based on Index Neighbor Structure
As seen from Table 2, the total storage load corresponding to the MK-Mediod algorithm was the smallest and the total storage load corresponding to the K-Mediod algorithm was the largest for different co-storage mapping record ratios. The total storage load increase in the MK-Mediod algorithm was 57.1% of the MGreedy algorithm, 8.1% of the Greedy algorithm, and 0.8% of the K-Mediod algorithm. Both the table and the above figure reflect the difference in the number of index neighbor groups constructed by these four algorithms. The K-Mediod algorithm constructs the largest number of index neighbor groups, which results in the largest total system storage and the largest increase in total storage load. The MK-Mediod algorithm, conversely, construct the smallest number of index neighbor groups, and certainly, the smallest increase in total storage load. Total storage load increases as the ratio number increases, but the comparison of the total storage load for the four algorithms stays stable regardless of the value of the ratio. #### 3.3.2 Average Service Latency and Maximum Service Latency

Figure 8 compares the average resolution service latency when the constructed index neighbor is used for collaborative resolution. The situation without collaborative resolution was used as a benchmark for comparison. As shown in Figure 8, the average service latency was smallest when no index neighbor structure was used, or when index neighbor structure constructed by K-Mediod algorithm was used. The average service latency with index neighbor structure constructed by Greedy, MGreedy, and MK-Mediod algorithms does not differ significantly when the system size is small, while the average service latency corresponding to the MK-Mediod algorithm was significantly smaller when the node size increased. The reason is that, when index neighbor structure is not used for co-resolution, users' resolution requests are directly responded by the master node without any redirection, \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **2\%** & **5\%** & **10\%** & **20\%** \\ \hline Null & 100,000 & 100,000 & 100,000 & 100,000 \\ MK-Mediod & 108,000 & 120,000 & 140,000 & 180,000 \\ MGreedy & 114,000 & 135,000 & 170,000 & 240,000 \\ Greedy & 198,000 & 255,000 & 590,000 & 1,080,000 \\ K-Mediod & 1,100,000 & 2,600,000 & 5,100,000 & 10,100,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Total storage load at different co-storage ratios. Figure 8: Average resolution service latency of each algorithm with different network sizes. the service latency is the smallest. The number of index neighbor groups constructed by the K-Mediod algorithm was the largest. When the system node size was set to 500, the K-Mediod algorithm will construct 500 index neighbor groups, which means every resolution node in level 2 will be one index neighbor group by itself. In this situation, for a specific user, \(u_{i}\), if its corresponding resolution node in level 2 is \(RN_{i+1}\), when the resolution request send by \(u_{i}\) cannot be fulfilled at the master node, it will be redirected to \(RN_{i+1}\), which is the nearest resolution node to \(u_{i}\) in level 2, and certainly, this structure will result in a smaller service latency. For MK-Mediod, MGreedy, and Greedy algorithms, in index neighbor groups constructed by the MK-Mediod algorithm, nodes in one group are closer to each other, which means a user could find a closer index neighbor for collaborative resolution, and the other two greedy algorithms do not take this into account, thus the service latency corresponding to MK-Mediod algorithm is smaller. Meanwhile, we compared the maximum service latency when these constructed index neighbors were used for a collaborative resolution. As shown in Figure 9, the maximum service latency distributes from 99.5 ms to 100 ms, and the latency increased with topology size.