# Adjacency-Information-Entropy-Based Cooperative Name Resolution Approach in ICN
## 4 Proposed Approach
### Latency Neighbor Maintenance
The reason is that the increasing number of parsing nodes that reach full load, and some name resolution requests that could have been processed locally are forwarded by these nodes to latency neighbors. Among them, the AN strategy imposes the largest additional load, while several other strategies are closer. Figure 6b also shows the standard deviation of a load of each name resolution node in the NRS under each strategy. Figure 5: Name resolution success rate comparison for each algorithm with different name resolution request rates. The AN strategy exacerbates the impact caused by the scale-free characteristic in terms of load and has the worst balance. The RN strategy has the best balance among the strategies that use latency neighbor cooperative name resolution. ACNR and DP are in between, with LNCR being relatively more advantageous. ACNR uses ILN to better quantify the structure of latency neighbors, reflecting a balance that is likely to be the reason for the superiority of the name resolution success rate over the DP strategy. It can be noticed that the LR strategy which does not use latency neighbors to cooperate has the best balance. The reason is that in scale-free networks, the distribution of the latency neighbors is affected by the power law property, so the strategies using latency neighbors have some negative impact in terms of load balancing more or less. #### 5.3.3 Parameter Discussion

In addition to the comparison with other strategies, we further explore the parameters in the ACNR approach. As shown in Figure 7a, the name resolution success rate has a positive correlation with the number of nodes selected at the latency neighbor cooperation, but as the value of \(k\) increases, the increase in the success rate of resolution becomes small. Figure 7b shows that the total number of name resolution requests in the network is positively correlated with the value of \(k\). In practice, the value of \(k\) for ACNR does not need to be set too large, and the specific characteristics of the network should be fully considered, weighing the name resolution success rate and the overhead of cooperative name resolution requests. We conducted the same experiment to compare the name resolution success rate with different parameter \(k\) of the DP strategy, as shown in Figure 8a, and the RN strategy, as

Figure 6: Load metrics comparison for each strategy with different name resolution request rates. (**a**) Total request number with different name resolution request rates. (**b**) Standard deviation of the loads on name resolution nodes comparison with different name resolution request rates. Figure 7: Experiment result of parameter \(k\) in ACNR approach. (**a**) Name resolution success rate comparison for each \(k\) with different name resolution request rates. (**b**) Total request number comparison for each \(k\) with different name resolution request rates. shown in Figure 8b. The experiment results present the positive correlation between success rate and parameter \(k\) as well. Considering that the effect trend of a change in \(k\) is essentially the same for all algorithms and the benefit brought by a larger \(k\) value is too low, we finally selected \(k=4\) as the parameter tuning result for each comparison algorithm for experiment in this paper. ## 6 Conclusions

The deterministic latency name resolution proposed in LNMRS is a very important feature to meet the demanding performance requirements of future networks. To achieve this feature, LNMRS imposes a strict constraint on the partition of the resolution domain based on the latency, and the name resolution success rate of users is not high enough to fully exploit the potential of the NRS. To address this problem, we propose the ACNR approach, which includes the discovery of latency neighbor and the cooperative name resolution based on it. ACNR uses the nested tree structure of LNMRS for latency neighbor initialization and dynamically updates them according to the transferability characteristic.