## 4 Hierarchical Cache Management (HCM) Policy
### SSD Cache Replica Management
In contrast, the PI, uCache, and HCM policies significantly reduce the SSD write load by introducing filtering mechanisms. Specifically,

Figure 4: Performance of different replacement policies under various traffic groups: (**a**) cache hit rate, (**b**) network link load, and (**c**) SSD write operations. the PI policy has the lowest write load but may lead to the issue of underutilized SSD resources. The write load of HCM is slightly higher than that of uCache, primarily because uCache determines replica popularity based solely on local statistics, while HCM more accurately identifies popular replicas by utilizing feedback from the service nodes, resulting in better caching performance. Nevertheless, HCM's write load remains at a relatively low level, balancing efficient SSD usage with improved cache performance. #### 5.2.2 Results Analysis Based on Real-World Traces

To further verify the effectiveness of HCM in real network scenarios, we use the real YouTube traffic load collected by [53], which includes a set of traces collected on different dates, corresponding to different monitoring durations, and different numbers of requests and video clips. We selected three sets of traces, and their main parameters are shown in Table 4. Similar to the settings in Section 5.2.1, we use LCE as the default cache placement strategy and set the cache capacity to 5% of the total content. Additionally, to simplify the analysis, we set the size of all video clips to a uniform 2 MB. The experimental results are shown in Figure 5.

As shown in Figure 5a, LFU exhibits the lowest cache hit rate across different traces. This is because, compared to the synthetic traces from GlobeTraff, the real YouTube trace has a less skewed distribution of popularity, reducing LFU's effectiveness. Additionally, the traces in this experiment were collected over a concentrated period, resulting in strong temporal locality in the request sequence, which explains why LRU achieves a higher hit rate. Since HCM uses LRU as its base management queue, its hit rate is similarly satisfactory and slightly outperforms PI and uCache. This result is consistent with the findings from the experiment using synthetic traces generated by GlobeTraff (see traffic group D in Figure 4a): when traces exhibit strong temporal locality, LRU's advantage is amplified, while LFU's performance declines. The advantage of HCM lies in its ability to maintain stable performance across different traffic loads. Figure 5b shows the network link load, which is basically inversely correlated with the cache hit rate. Figure 5c illustrates \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Trace** & **Number of Video Clips** & **Number of Requests** & **Duration (h)** \\ \hline T4 & 82,132 & 145,140 & 162 \\ T5 & 303,331 & 611,968 & 336 \\ T6 & 131,450 & 243,023 & 168 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: YouTube traffic traces. Figure 5: Performance of different replacement policies under YouTube traffic traces: (**a**) cache hit rate, (**b**) network link load, and (**c**) SSD write operations. the SSD write load. Although HCM does not significantly outperform LRU in terms of cache performance, it achieves the same results with fewer SSD write operations. #### 5.2.3 Results Analysis Under Different Cache Placement Strategies

The replica placement strategy controls both the location and quantity distribution of replicas, while the replica replacement policy is responsible for managing the local cached replicas. Together, they determine the overall distribution of cached replicas within the network. A good replacement policy should maintain stable performance across different placement strategies. To this end, we selected several typical replica placement strategies, including LCE, LCD (Leave Copy Down) [48], CL4M (Cache Less for More) [54], Random [16], and ProbCache [55], and studied their cache performance when combined with different replacement policies.