## 2 Materials and Methods
### MK-Mediod Algorithm
#### 2.3.3 Collaborative Storage and Resolution based on Index Neighbor Structure
Nodes in level 2 are all children of the master node, each with a random number of users assigned. The minimum one-way latency between the master node and nodes in level 2 is 0.5 ms and the maximum is 25 ms, the minimum one-way latency among the resolution nodes in level 2 is 5 ms and the maximum is 30 ms, the minimum one-way latency between the user and the corresponding level 2 resolution node is 0.5 ms and the maximum is 25 ms, the minimum one-way latency between the user and the level 1 resolution node is 25 ms and the maximum is 50 ms, and dijkstra tool in the networkx toolbox is used to generate the shortest point-to-point latency matrix. In this experiment, we set the size of the resolution nodes as 50/100/150/200/300/500, and compare the latency measurement cost and computation cost of our index neighbor construction algorithm against several other approaches, and also, the increase of total storage load and service latency when the constructed index neighbor structure is used to implement collaborative storage and resolution. The settings of the experiment are also shown in Table 1.

The experimental environment was created and run in Python 3.6 on a computer with an Intel Core (TM) i7-9750H CPU and 16 GB RAM. For each set of experiments, we ran 20 times independently and analyzed the results. In each round of experiments, the algorithms were run on the same name resolution system topology, with the same latency among nodes and with the same mapping record allocation. The main metrics recorded in the experiments include latency measurement times, the number of sorting operations, the total storage load after load balancing using index neighbors, the average service latency and maximum service latency when resolving mapping records using index neighbors. ### Construction of Index Neighbor Structure

As the network size increases, the matrix involved in the index neighbor construction process might increase in equal proportion. The matrix also varies for different index neighbor construction algorithms. Several neighbor discovery ideas are replicated in the experiments. The neighbor discovery algorithms used for comparison are as follows. * Greedy algorithm [32]: This algorithm traverses each child node, uses the traversed child nodes to form an index neighbor group and tries to pull the rest of the child nodes into the group, this procedure is performed recursively until every child node resides in an index neighbor group. * Modified Greedy algorithm: Different from the Greedy algorithm, this algorithm firstly sorts the child nodes by latency to the master node, and then greedily constructs index neighbor groups in the same manner with the Greedy algorithm in decent order of latency to the master node. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of resolution nodes & 50/100/150/200/300/500 \\ Deterministic latency parameters & 50 ms, 25 ms \\ Latency scope from master node to nodes in level 2 & [0.5 ms, 25 ms) \\ Latency scope among child nodes & [5 ms, 30 ms) \\ Total number of contents & 100\({}^{\prime}\)(number of resolution nodes) \\ Ratio of contents to be co-stored & 2\%/5\%/10\%/20\% \\ Contents for resolution & all of the contents on master node \\ Latency scope from master node to users & [25 ms, 50 ms) \\ Latency scope from resolution nodes in level 2 to users & [0.5 ms, 25 ms) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Evaluation setting. K-Mediod algorithm [33]: Like our proposed MK-Mediod algorithm, this algorithm clusters the child nodes according to their latency to each other, and validates whether the clustering results meet the latency constraints defined by index neighbor groups. Clustering number increases one by one until a feasible resolution is found.