## 4 Hierarchical Cache Management (HCM) Policy
### SSD Cache Replica Management
In this experiment, we tested the cache performance of different replacement policies under each traffic group shown in Table 2. To control variables, LCE (Leave Copy Everywhere) [48] was used as the default cache placement strategy, which caches a copy of the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Traffic Group** & **Web** & **Video** & **Other** \\ \hline A & 10\% & 50\% & 40\% \\ B & 20\% & 50\% & 30\% \\ C & 30\% & 50\% & 20\% \\ D & 40\% & 50\% & 10\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Traffic composition of mixed workloads in experimental scenarios. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Traffic Type**} & \multicolumn{2}{c}{**Popularity Distribution**} & \multirow{2}{*}{**Time Locality**} \\ \cline{2-3} \cline{5-5}  & **Model** & **Parameters** & \\ \hline Web & Zipf & \(\alpha=0.75\) & LRU stack model [52] \\ Video & Weibull & k = 0.513, \(\lambda=6010\) & Random Distribution \\ Other & Zipf & \(\alpha=0.75\) & Random Distribution \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Model parameters for different traffic types in Globetraff. content at each hop node along the content delivery path. The cache capacity was set to 5% of the total number of contents. The experimental results are shown in Figure 4.

Figure 4a shows that under different input traffic loads, HCM consistently achieves the highest cache hit rate. In contrast, classical replacement policies such as LRU and LFU exhibit significant performance differences across different traffic loads, indicating that they are only suitable for specific application scenarios. For example, in traffic group A, where the proportion of Web traffic is low and the request temporal locality is weak, LRU performs poorly. However, in traffic group D, as the proportion of Web traffic increases, LRU's performance improves significantly. Conversely, LFU shows an entirely opposite trend in performance across these two traffic groups. The PI policy, which only inserts replicas that hit in the DRAM cache into the SSD, struggles to effectively handle scenarios with longer request intervals in Video and other traffic types, resulting in a lower hit rate. In contrast, both uCache and HCM more effectively monitor changes in replica popularity by introducing virtual queues to record the most recently evicted replicas from the DRAM cache. HCM outperforms uCache in several aspects. First, HCM facilitates more accurate SSD cache admission filtering by transmitting content popularity information between nodes, ensuring that popular replicas are identified more accurately. Second, HCM introduces a redundancy estimation mechanism, further improving the hit rate. These improvements enable HCM to demonstrate outstanding cache performance across all traffic loads. Figure 4b presents the experimental results for the network link load, which shows a significant negative correlation with the cache hit rate: the higher the cache hit rate, the lower the network link load. This is because a higher cache hit rate allows more data requests to be served directly by cache nodes that are closer, greatly reducing the amount of data transmitted through the network links. In addition, shorter response distances not only reduce bandwidth consumption but also decrease response latency, which is particularly important for latency-sensitive applications. Due to its advantage in cache hit rate, HCM consistently achieves the lowest link load across all traffic loads, significantly optimizing bandwidth utilization and improving overall network performance. Figure 4c clearly demonstrates the important role of SSD cache admission filtering in reducing SSD write load. Since the LRU and LFU policies directly write replicas to the SSD cache without any filtering, this leads to a very high SSD write load, which not only accelerates the SSD wear and significantly shortens its lifespan but also increases the processing overhead on the cache nodes.