## 4 Hierarchical Cache Management (HCM) Policy
### SSD Cache Replica Management
Under traffic group C, LFU's cache hit rate is notably lower than that of other strategies, mainly because this group has a higher proportion of Web traffic, which leads to strong temporal locality between requests. However, LFU demonstrates a higher growth rate as cache capacity increases. This is because, with more cache capacity, LFU can retain more popular replicas, and in scenarios where popularity follows a heavy-tailed distribution, it can hit more requests. Figure 6: Performance of different replacement policies under various cache placement strategies: (a) cache hit rate, (b) network link load, and (c) SSD write operations. Figure 7: Cache hit rate vs. cache capacity under different replacement policies: (a) results for traffic group B; (b) results for traffic group C.

As shown in Figure 8, with the increase in cache capacity, the link load of all replacement policies decreases, and overall, there is a negative correlation with the cache hit rate. Under both traffic loads, HCM consistently achieves the lowest link load. Although LFU's cache hit rate grows faster as cache capacity increases (as shown in Figure 7), HCM still maintains a significant advantage in terms of link load. In addition, compared to uCache, HCM's link load decreases more rapidly as cache capacity increases, thanks to its faster cache hit rate growth. The multi-queue replica management mechanism in HCM allows for more efficient utilization of the expanded cache space, while uCache uses a single LRU queue to manage the SSD cache, leading to limitations in SSD space utilization. Figure 9 shows the changes in SSD write load as cache capacity increases. With the increase in cache capacity, the SSD write load of different replacement policies slightly decreases, mainly due to the improvement in the cache hit rate and the shortening of the request response distance, which alleviates the overall cache load. However, the primary factor determining the SSD write load remains the SSD admission filtering mechanism. As a result, the SSD write load of LFU and LRU remains consistently high. In contrast, HCM, uCache, and PI effectively filter the replaced replicas in DRAM based on replica popularity information, maintaining a consistently low SSD write load. In addition, HCM includes a cache load tuning option that allows the SSD admission rate to be adjusted based on demand, further reducing the SSD write load. ## 6 Conclusions

This paper proposes an ICN cache management policy for hierarchical cache architectures, which fully exploits the advantages of large-capacity SSDs by combining the differentiated read-write features and workload characteristics of each cache layer. First,

Figure 8: Network link load vs. cache capacity under different replacement policies: (**a**) results for traffic group B; (**b**) results for traffic group C.

Figure 9: SSD write load vs. cache capacity under different replacement policies: (**a**) results for traffic group B; (**b**) results for traffic group C.

we design and implement differentiated cache replacement policies for the different data access patterns observed at each cache layer. At the DRAM layer, we use LRU to cache short-term popular data; at the SSD layer, we use a multi-queue replacement policy based on cache utility to prioritize caching content that provides higher cache benefits. Next, we design a replica admission filtering mechanism for SSDs, utilizing the redundancy and popularity information of replicas in the ICN cache to filter out low-benefit content. This approach effectively reduces the SSD write load without sacrificing the cache hit rate, thereby reducing its operational costs. However, the experiments also indicate that different cache placement policies have a significant impact on the final results, highlighting the clear interaction between cache placement and replacement policies.