## 2 Related Work
### ICN Cache Replacement Policies  
Cache replacement policies have been widely studied in fields such as operating systems and web caching. These classical policies optimize replacement decisions by considering different statistical information of cached items [24]. For example, LRU (Least Recently Used) replaces the least recently accessed cache item based on access time, with variations including SLRU [25], LRU-K [26], and q-LRU [27]. Frequency-based policies, such as LFU (Least Frequently Used), LFU-Aging [25], and FB-FIFO [28], aim to retain popular data in the cache to improve cache hit rates. Additionally, hybrid policies, such as ARC [29], LRFU [30], and 2Q [31], combine multiple replacement algorithms to adapt to different application scenarios. Policies like GreedyDual-Size [32] and LNC-R-W3 [33] take into account the access cost of cached items, improving the accuracy of replacement decisions by considering both the size and access frequency of cached items. In addition to following these classical replacement algorithms, ICN in-network caching has further developed cache replacement policies to address the ubiquitous nature of ICN networks, considering node- and network-level attributes [14]. For example, ref. [34] proposed using heterogeneous replacement algorithms on edge and intermediate routers to handle different observed workload characteristics. Ref. [35] employed an LRFU (Least Recently and Frequently Used) policy with weighted parameters, allowing each content router to implement different caching policies based on its position in the network. Ref. [36] discussed the benefits of applying different cache replacement policies at each layer of the data center fat-tree topology for big data application scenarios. Similar work can be found in [37; 38]. Ref. [39] proposed a neighbor cooperation-based cache replacement policy aimed at utilizing neighboring nodes' cache resources to extend the caching time of replicas. Additionally, some studies focus on specific application scenarios. For instance, ref. [40] proposed a freshness-priority replacement policy for NDN-based IoT (Internet of Things), where data are evicted based on predictions of invalid data from the time series of future sensor events. Ref. [41] introduced a QoS-aware cache replacement policy for VNDN (Vehicular Named Data Networking), where traffic is classified and stored in different sub-caches. Ref. [42] proposed a replacement policy for the IoT scenario based on three fundamental content attributes: request count, remaining lifetime, and the waiting time for upcoming requests. However, the lack of effective hierarchical cache management policies remains one of the key factors limiting the widespread application of SSD-based large-capacity storage in ICN in-network caching [8]. Ref. [43] pointed out that caching in persistent memory or disk storage was the future direction and emphasized the necessity of designing new caching algorithms in multi-layer caches that took into account the characteristics of different devices. In a hierarchical cache architecture, cache replacement not only manages local replicas to enhance cache performance but also needs to consider the read/write characteristics of different storage devices to balance cache benefits and usage costs. For example, ref. [10] pointed out that each SSD block could only undergo a limited number of writes and erasures during its lifecycle. The authors proposed a lightweight cache insertion policy called Probationary Insertion, where DRAM and SSD cache spaces were managed with independent LRU replacement policies. Content is only inserted into the SSD if it has been requested at least once during its time in DRAM. Similarly, uCache [15] designed a two-level LRU chain filter to distinguish one-time accessed data blocks, aiming to avoid unnecessary writes to the SSD. Ref. [44] noted that a two-level cache hierarchy, where each level uses independent cache replacement policies, results in inefficient cache resource management and reduced system performance.