# Adjacency-Information-Entropy-Based Cooperative Name Resolution Approach in ICN
## 4 Proposed Approach
### Latency Neighbor Maintenance
#### 5.2.2 Overhead

Pursuing only latency neighbor recall ratio is more one-sided because different latency neighbor discovery algorithms are accompanied by different overheads during execution, and it is not economical or reasonable to spend a large amount of overhead to improve a small amount of latency neighbor recall ratio. We focus on the communication overhead, which specifically includes the communication overhead of querying the index and structural relationships of a name resolution node to other nodes during the execution of an algorithm and the communication overhead of latency measurements among name resolution nodes. Figure 2: Latency neighbor recall ratio comparison for each algorithm. (a) Latency neighbor recall ratio with different discovery rounds at network size of 1500; (b) latency neighbor recall ratio with different network sizes at discovery round of 4. Figure 4 shows the total number of neighbor discovery messages in the network at different discovery rounds for a network size of 1500. The GM algorithm requires measuring the latency between every two nodes in the network, and although it can discover all latency neighbors in a global perspective, its measurement overhead is O(n\({}^{2}\)) to the network size, and the scalability becomes extremely poor at larger network sizes. The PF algorithm has an exponentially increasing overhead as the diffusion range increases and has exponentially increasing overhead, again with poor scalability. LG and RW make the overhead relatively small due to limiting the number of branches per hop diffusion. The ACNR approach has worse overhead than LG and RW, but it does not have a significant increase with the number of discovery rounds, indicating that ACNR has a smaller overhead share in the periodic latency neighbor discovery process and can support dynamic latency neighbor discovery compared with other algorithms. It is important for name resolution nodes to keep dynamic latency neighbor discovery in the process of providing services to users. Discovering new latency neighbors and stopping using invalid latency neighbors are the guarantee of efficient neighbor cooperation. ACNR does not bring too many discovery messages in the multiple round latency neighbor discovery processes. The controllable discovery round and the limitation of discovery information in local areas also make the maintenance of neighborhood structure in ACNR not bring too much pressure to the packet transmission and processing of name resolution nodes. Taking all these considerations into consideration, ACNR can discover more latency neighbors with reasonable overhead, which is an efficient discovery algorithm. Figure 4: Discovery message number comparison for each algorithm with different discovery rounds at a network size of 1500. Figure 3: Neighbor recall ratio comparison for each algorithm with different discovery rounds at the middle level. ### Neighbor Cooperative Name Resolution

After obtaining the latency neighbors, a name resolution node has the ability to use these latency neighbors for cooperative name resolution. The usage strategy is closely related to the overall service capability of the NRS. We replicate several existing algorithms or ideas and apply them to the strategy of using latency neighbor to find the best performance strategy. The comparative algorithms are as follows:

* Local Resolution (LR) [24]: This strategy does not use latency neighbors for cooperative name resolution, and each name resolution node only resolves the user's name resolution request locally. * All Neighbor (AN): In this strategy, to maximize the range of name resolution requests and thus increase the success rate of name resolution, each name resolution node forwards a name resolution request to all latency neighbors when it does not resolve the requested name mapped address locally.