## 2 Materials and Methods
### MK-Mediod Algorithm
#### 2.3.3 Collaborative Storage and Resolution based on Index Neighbor Structure
During the collaborative storage process, this experiment generates _number of system nodes_\(*\) 100 mapping records and randomly selects level-2 resolution nodes for registration, and the registration is then passed to the master node afterwards. During the collaborative resolution process, this experiment take 20% of the mapping records of the master node for co-storage, and resolves all mapping records on the master node from randomly selected users. Figure 5: Number of sort operations of each algorithm with different network sizes. Figure 6: Number of sort operations of each algorithm with different network sizes—detailed comparision. #### 3.3.1 Total Storage Load

(a) Impact of number of resolution nodes

We set the number of resolution nodes as 50/100/150/200/300/500, and construct index neighbor structure with Greedy, MGreedy, K-Mediod, and MK-Mediod algorithm. Afterwards, the constructed index neighbor structure is used for collaborative mapping records storage. Here, we take 20% mapping records from the master node for co-storage, and compare the total storage load of the system. The situation without co-storage is used as a benchmark for comparison. As seen from Figure 7, the K-Mediod algorithm corresponds to the greatest total storage load, the Greedy algorithm is the second, and the MK-Mediod algorithm corresponds to the least total storage load. The reason is that, in the collaborative storage method based on index neighbor structure, each index neighbor group will be assigned one copy of the mapping records to be co-stored for the purpose that users of each index neighbor group could resolve these mapping records within a determined service latency. If the number of index neighbor groups is too large, it will cause a large increase in the total storage load. K-Mediod algorithm does not consider the latency to the master node in the clustering process, so the elected cluster center does not represent the best node position, and the clustering effect is not ideal. In one round of the cycle, the number of index neighbor groups needs to be increased as long as one clustering group does not pass the following validation process, so the number of index neighbor groups generated by K-Mediod algorithm is the largest, and the corresponding total storage load is also the largest. The two greedy algorithms build index neighbor groups in a greedy manner, and the subsequent index neighbor group constructing process will not affect the previous constructed index neighbor groups, so the number of index neighbor groups built is less than that of the K-Mediod algorithm, which corresponds to less total storage load. The MGreedy algorithm firstly selects nodes close to the master node as an index neighbor, and the \(latency(indexNe^{k},RN_{i+1}^{t})\) part in Equation(6) is then more easily fulfilled, so these nodes have a higher probability of building larger index neighbor groups, which will result in fewer index neighbor groups, and correspondingly, less total storage load. In the clustering process, the MK-Mediod algorithm considers not only the latency between child nodes, but also the latency to the master node, so the clustering results are more easily passed in the following validation procedure. The construction result is the best of the four algorithms, which means the test number of index neighbor groups and the smallest total storage load. (b) Impact of numbers of mapping records for co-storage

We set the resolution system node size as 500. Four algorithms of Greedy, MGreedy, K-Mediod, and MK-Mediod were used to construct index neighbors, and the constructed index neighbor structure was used for collaborative mapping records storage. Here, the ratio of mapping records on the master node for co-storage was set to 2%, 5%, 10%, and

Figure 7: Total storage load of each algorithm with different network sizes. 20%. We compared total storage load in these situations to explore the impact of numbers of mapping records for co-storage. Situation without co-storage was used as a benchmark for comparison.