## 6 Results
### Experimental Setup  
A classical ICN network topology GARR (Italian academic network) is selected for simulation. The experiment is completed on a Python-based ICN simulation platform. Table 2 shows the basic parameters for our simulation. There are 104 nodes in the network, 13 of which are source nodes connected to the content server, 20 of which are edge nodes connected to users, and the others are intermediate nodes. All content servers store \(3\times 10^{5}\) content that satisfies the Zipf distribution expressed as \(p_{i}=\frac{i^{-\alpha}}{\sum_{i=1}^{\infty}k^{-\alpha}}\). The popularity of content i is determined by both the parameter \(\alpha\) and the content set size C. Of the 13 content servers, we randomly selected two as large servers with a weight of 80; one as a medium server with a weight of 25; and the other as small servers with weights ranging from 1 to 10. We place the contents in 13 content servers according to the above weights, and use Expression (2) to calculate the weights of each content domain, Expression (3) to calculate the weights of each data path, then use Expression (5)-(8) to calculate the global weights of each node, and finally allocate cache space for each node based on its weights. Requests from all users are generated with the same probability. To conform to the normal operation of the actual network, we first generated \(3\times 10^{5}\) requests to preheat the system so that the empty cache of each node in the network stores hot network content. We then generated \(6\times 10^{5}\) more requests to collect data. All the requests are independent. From the previous research [38; 39], the Zipf distribution parameter \(\alpha\) on web content ranges from about 0.6 to 0.9, so we set \(\alpha\) based on this range. We use the ratio of total cache size to total content size to represent the total cache budget rather than its absolute value, which represents the relative amount of cache space to the total content. We use 0.01 to 0.11 as the value range for the total cache budget. We recorded the cache hit ratio of each cache node in each experiment and calculated the average cache hit ratio, which is the clearest indicator of cache system performance. Average latency is also measured in each experiment to show how the caching system improves the user experience. In addition, we recorded the amount of content delivered per unit time of each content server, which can reflect the traffic load of the content server. In cache allocation scenarios, LCE (Leave Copy Everywhere) cache content placement strategies are often used as default strategies in ICNs. This scheme is simple and easy, but it causes a lot of unnecessary redundancy in the cache on the network, so LCD (Leave Copy Down) [40] content placement strategy is often used instead of LCE to reduce redundancy in content copy. We experimented with the LCD placement strategy and compared the effects of different cache replacement policies on cache size allocation schemes through LRU (Least Recently Used) [41] and LFU (Least Frequently Used). LRU will replace content that has not been accessed for the longest time in the most recent period with new content, while LFU will replace content that has been accessed least in the most recent period. We use three traditional cache allocation schemes, including homogeneous cache allocation scheme (represented as UNIFORM) and two heterogeneous cache allocation schemes, degree centrality based cache allocation (represented as DEGREE) and betweenness centrality based cache allocation (represented as BETWEENNESS), for comparison with the hierarchi