## 5 Performance Results of RACD Strategy
### Simulation Environment  
The performance evaluation of the proposed strategy was performed in two steps. First, we implemented the classification model to classify the dataset used in the simulation. Then, the proposed strategy was evaluated through simulation. Due to the lack of standard datasets in NDN, we constructed our dataset by combining the DMOZ (DMOZ: [[https://web.archive.org/web/20170312160530/http://rdf.dmoz.org/rdf/content.rdf.u8.gz](https://web.archive.org/web/20170312160530/http://rdf.dmoz.org/rdf/content.rdf.u8.gz)]([https://web.archive.org/web/20170312160530/http://rdf.dmoz.org/rdf/content.rdf.u8.gz](https://web.archive.org/web/20170312160530/http://rdf.dmoz.org/rdf/content.rdf.u8.gz)) (accessed on 12 December 2021)) dataset with links extracted from the website of the University Amar Telidji Laghouat (UATL). DMOZ is a well-known dataset for classification, and it is used in many works as in [20; 21]. The names of the DMOZ dataset are labeled as data belonging to the class \(\{0\}\) (unimportant), and the links extracted from the UATL are labeled as data belonging to class \(\{1\}\) (important). The dataset used is divided into two parts, one for the training and the other for testing the model. The implemented Multinomial Naive Bayes classifier model has achieved 98% accuracy in classifying the content names. After that, the RADC strategy is evaluated through ndnSIM [22], which represents the official ns-3-based simulator deployed by the NDN community. ndnSIM enables reliable simulation experiments that can be replicated in real environments without having to modify the source code [23]. To make our simulation as realistic as possible, we chose a real dataset from the University Amar Telidji Laghouat (UATL) as a realistic interest consumption. The UATL dataset contains more than one million links consumed during a week at this university. The strategies are simulated using a non-complete K-ary tree as in [24; 25; 26]. This topology is based on two parameters, D and k, where they represent consecutively the depth of the tree and the number of children of each node in the tree. We chose \(D\in[3,7]\) and \(k\) in the interval \([0,5]\), where \(k\) is randomly generated for each node as in [26]. For the simulation scenario, we assume that there are two root nodes, where the first is a server that belongs to the university, and it contains all its data. The second represents a server containing all other data requests. Furthermore, we assume that all requests are sent by leaf nodes. For each test run, users are assumed to express interests from the used UATL dataset. For simplicity, we set a similar cache size for all nodes in the network. Each link in our topology has a bandwidth of 10 Gbps, which is greater than the traffic demand, and a propagation delay of 1 ms. Table 2 shows the choice of the main parameters for our simulation. For the evaluation method, we studied the impact of parameters priority level, cache size, and tree depth on the caching performance in the network. We compare the results of our resource allocation mechanism with the mechanism that promotes content sharing in the same cache. The sharing mechanism represents the majority of the proposed strategies, with a difference in the choice of the placement and replacement strategy. Since RADC does not apply any particular technique to manage the cache cont \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Default Value** & **Range** \\ \hline Tree-ary k & - & [0;5] \\ Tree depth D & 5 & [3;7] \\ node cache capacity & 100 & [10;100] \\ Priority level & 0.4 & [0.1;0.9] \\ Delay & 1 ms & - \\ Bandwidth & 10 Gbps & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameter settings for the simulation. easily be adopted. We compare the two strategies in the same environment, applying the same placement and replacement technique. The two strategies represent, consecutively, LCE [3] and LRU [19] for placement and replacement.