## 3 Intelligent Content Prefix Classification Techniques
### Performance Evaluation and Discussion  
After the content prefix classification has successfully performed and shaped into valuable content attributes as content name, the name prefix is ready to be dispatched into ICN. Notably, upon receiving a request packet, ICN intermediate router first checks its content store and then pending interest table, as stated in typical ICN forwarding [1]. To evaluate the benefits of the classification methods, we simulate our proposal using ndnSIM [24], a widely-used emulator of name data networking for ICN platform under the ns-3 framework. The network topology used in this simulation is five-layer tree topology as depicted in Figure 7. We assume that the root node at first layer acts as the data center located and it is connected to three core content router nodes in the second layer. At the third layer, these core nodes connect to five edge nodes, which further connect to five aggregated nodes in the fourth layer. Users in the same area connect to their respective aggregated node and send requests (interest packets) for interested content in ICN, given that all router nodes are implemented with the full function as an ICN node (NDN protocol). Using this topology, we simulate two scenarios of interest-data communication. For the first simulation, we implement content request using an unclassified prefix, whereas, for the second simulation, we implement the already classified prefix using our intelligent framework. In both scenarios, each node has three NDN fundamental data structures (CS, FIB, and PIT) with the same size of CS for cache storage. For simplicity, we also assume that all content objects have the same size. For each scenario, the simulations were executed using two different interest arrival rates. In the first part, user nodes generate a stable, uniform distribution rate of 10 Interest packets per second, whereas, in the \begin{table}
\begin{tabular}{l l} \hline \hline
1: & Function GA-SA Discovery Component () \\
2: & Initialize GA Population of Chromosome \\
3: & Initialize SA Temperature \\
4: & **For** each iteration \\
5: & Set mutation and crossover \\
6: & Evaluate the cost function \\
7: & Calculate priority vector by AHP \\
8: & **End For** \\
9: & **If** termination criteria are not achieved, **then** \\
10: & Select a pair of Chromosome for Mating \\
11: & **For** it = 1: MaxIteration \\
12: & **// Perform Crossover** \\
13: & **For** Subliteration = 1: MaxSublIteration \\
14: & Perform Crossover \\
15: & Evaluate Offspring \\
16: & **End For** \\
17: & **// Perform Mutation** \\
18: & **For** Subliteration = 1: MaxSublIteration \\
19: & Perform Mutation \\
20: & Evaluate Mutants \\
21: & **End For** \\
22: & **End For** \\
23: & **End If** \\
24: & Merge Offspring’s in the Population \\
25: & Sort New Population \\
26: & Compare New Population using SA Rule \\
27: & Update New Population \\
28: & Temperature Reduction \\
29: & Update Best \\
30: & Solution Found Store Best Cost \\ \hline \hline \end{tabular}
\end{table} 
Table 4: GA-SA Discovery Components Pseudo-code. second simulation, we use Zipf-Mandelbrot for modeling content popularity distribution with 10 interest packets per second as the request frequency. The simulation time used for both cases is 100 s.  
Simulation under a uniform distribution of the Interest packet arrival rate shows that the classified content prefix achieves lower packet drop as well as lower network resource utilization, as shown in the Figures 8 and 9, respectively. The similar tendency is also observed in Figures 10 and 11 when Zipf-Mandelbrot modeled the interest frequency distribution rate. The results show that the proposal achieves lower packet drop rate as well as lower network resource utilization for interest packets by using the classified content prefix format, especially when the number of user increase.