## 4 Hierarchical Cache Management (HCM) Policy
### SSD Cache Replica Management
Experimental results demonstrate that this value not only keeps the SSD write load at a very low level but also ensures good cache performance. ### Simulation Results Analysis

In this section, we design three sets of experiments to evaluate the performance of HCM. Experiment 1 tests the applicability of different policies under different input traffic loads. Experiment 2 tests the performance of various replacement policies integrated with different cache placement strategies. Experiment 3 compares the scalability of different replacement policies by adjusting cache capacity. Each experiment is repeated 5 times, and the average is taken as the final result. A 95% confidence interval is also calculated for each metric to assess the statistical significance of the results. In each subsection, we will introduce the specific settings of each experiment. Below are the three cache performance evaluation metrics used in this section. (1) Cache Hit Rate

Cache hit rate is one of the key metrics for measuring the performance of a caching system. It represents the proportion of requests serviced by the cache system out of the total number of requests. A higher cache hit rate means that more requests can be served directly from the cache, eliminating the need to access the original server. This not only shortens data response distances but also reduces network latency and bandwidth consumption. By improving the cache hit rate, cache resources can be utilized more efficiently, which in turn enhances overall system performance and optimizes the user experience;

Figure 3: Visualization of the GARR Topology. Network link load refers to the amount of data transmitted over each link per unit of time. It is a key metric for evaluating the caching system's effectiveness in reducing network traffic costs. Typically, network link load is closely related to the cache hit rate--that is, the higher the cache hit rate, the lower the link load tends to be. This is because the cache system can more effectively utilize cached data, reducing the need to fetch data directly from the original server, thus lowering network bandwidth consumption and improving network scalability. In calculating the link load, we sum the loads of all used links and take the average as the final result, providing a comprehensive assessment of the caching system's efficiency in utilizing network resources;

SSD write load is represented by the total number of replicas written to the SSD cache. A reasonable SSD write load is crucial for extending the lifespan of the storage device. Cache replacement policies need to improve cache hit rates while minimizing unnecessary write operations, in order to maintain the health and long-term performance of the storage device. In calculating the SSD write load, we record the total number of replicas written to the SSD cache across all nodes during the simulation process. #### 5.2.1 Results Analysis Across Different Traffic Groups

To simulate scenarios with mixed traffic types in real-world networks, we use the traffic generator Globetraff [51], which generates mixed traffic workload including applications such as Web, P2P, and Video, based on specified traffic ratios and models. To validate the applicability of HCM to different traffic loads, we use Globetraff to generate four sets of traffic workloads with different mix ratios (as shown in Table 2), with the model parameters for each traffic type listed in Table 3. Data objects in ICN networks are typically divided into fixed-size data chunks [3; 4]. However, there is currently no strict standardization for the size of ICN data chunks, leading to significant variations in the chunk sizes published by different content providers. To simplify the experiments and analysis, we set the size of data objects for all traffic types in the experiment to 2 MB. In addition, all other unspecified parameters are consistent with the settings in reference [51].