# Adjacency-Information-Entropy-Based Cooperative Name Resolution Approach in ICN
## 4 Proposed Approach
### Latency Neighbor Maintenance
The basic parameters of our experiments are given in Table 2.

The experimental environment was created and run in Python 3.8 on a computer with an Intel Core (TM) i7-9750H CPU and 16 GB RAM. In each round of simulated experiments, the methods used for comparison applied the same topology and the same LNMRS structure, as well as the same random number generator seed. Additionally, the workload is set at the same to ensure fairness of the comparison. For each set of experiments, we ran 20 times independently and analyzed the results. ### Latency Neighbor Discovery

As the size of the network increases, the communication message overhead caused by the discovery of latency neighbor will be non-negligible traffic. How the name resolution nodes can discover as many nodes that can be their latency neighbors with as little communication overhead as possible is a key indicator of how good the latency neighbor discovery algorithm is. We replicate several existing algorithms or ideas and apply them to the problem of latency neighbor discovery in LNMRS. The comparative algorithms are as follows: \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Describe** \\ \hline Service level number & 3 \\ Latency upper bounds & 10 ms, 25 ms, 50 ms \\ Content number & \(10^{4}\) \\ Request number & \(5\times 10^{4}\) (\(10^{4}\) warm-ups) \\ Request distribute & Poisson \\ Request rate & 1000 req/s \\ Content request distribution & Zipf with lambda = 0.9 \\ Network topology & Barabasi–Albert scale-free model \\ Network scale & 100–1500 \\ Neighbor discovery branch number & \(m=4\) in the proposed strategy and \\  & \(m=6\) in other strategies \\ Selected latency neighbor number & \(ke[1,2,3,4,5,6]\), default 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation configuration. * Global Measurement (GM) [54]: This algorithm uses a centralized server to index all name resolution nodes, and a name resolution node can perform latency measurements on all other name resolution nodes to comprehensively discover all name resolution nodes that are eligible to be latency neighbors. In our experiments, the latency neighbors of each name resolution node given by the GM algorithm are used as a comparison benchmark for other algorithms. * Promoted Flooding (PF) [35]: The flooding approach is typical in distributed systems where each name resolution node sends latency neighbor discovery messages with hop limits to other network nodes connected in the network, and these messages are diffused recursively. When a name resolution node receives this message, it responds to the message and then the node that initiated the discovery determines the neighbor relationship between the two according to latency measurement. Here, we use a promoted flooding approach where the network node discards duplicate latency neighbor discovery messages received. * Random Walker (RW) [36]: This algorithm generates \(m\) search messages at each name resolution node, and each searches message randomly selects \(m\) neighbor nodes per step for latency neighbor discovery message forwarding, which achieves message spreading while attenuating the flood range of messages in the network. * Latency Greedy (LG) [55]: This algorithm is similar to the RW algorithm, but chooses \(m\) neighbor nodes with the shortest latency at each step for search message forwarding. The parameter \(m\) used in the ACNR strategy is set as 4, and the parameter \(m\) used in the RW and the LG strategies are set as 6. These parameter values were fine-tuned through multiple rounds of testing different strategies under our experimental setup, based on the balance of experimental results and overhead, etc. #### 5.2.1 Recall Ratio

To measure the performance of different latency neighborhood discovery algorithms, the average recall ratio is used as an evaluation criterion. The recall ratio of latency neighbor represents the ratio of available latency neighbors of a name resolution node obtained by a discovery algorithm to the number of truly all latency neighbors of that node in the network.