# Time Segmentation-Based Hybrid Caching in 5G-ICN Bearer Network
## 3 Path Segmentation-Based Hybrid Caching
### Caching in Low Liquidity Period
### Setting of Simulation Environment

For the cache strategy, we choose LCE, LCD, CL4M, and ProbCache, as the comparison cache placement strategy. Their specific introduction is as follows:

* Leave Copy Everywhere (LCE): LCE is a typical on-path cache. In this strategy, the content will be cached in all nodes it passes through. This caching strategy will cause a lot of redundancy of network content replicas, which will also lead to low network utilization. In addition, frequent content replacement also reduces ICN cache utilization [21]. * Leave Copy Down (LCD): the mechanism of this strategy is that when the content in the network hits, the replica will be copied down its path to the user. This will eventually place the content as close to the user's network edge as possible [22]. * Cache Less for More (CL4M): the strategy is triggered only once in the cache path, and the content is cached in the node with the largest mediation centrality [24]. * Probabilistic Caching (ProbCache): this strategy caches the contents in the cache path according to the probability. ProbCache attempts to reduce redundancy between network caches. That is, it aims to maximize the number of different content items cached along the delivery path [17]. We use Icarus as the simulation software [43]. Icarus is a Python-based ICN caching experiment simulator, licensed under the terms of the GNU GPLv2 license, which can be applied to various caching strategies and network topologies, and its object-oriented packaging supports flexible function and module expansion for experimenters. In addition, the Icarus simulator can support millions of warmups or requests in a short period of time. To fully measure the performance of our architecture, we chose the following cache performance metrics: cache hit ratio and latency. We choose LFU as the cache replacement algorithm. Since the gNodeB caching capability and the user's own D2D communication mechanism are not considered, in the simulation topology, we regard the internal node with degree 1 as the base station. The simulation parameters are shown in Table 2. To simulate a real-world scenario, the number of contents is approximately 10 times the number of

Figure 4: The process of determining whether a node is cached. requests, depending on the actual situation. We set the number of network contents to \(10^{5}\), the number of test requests to \(10^{4}\) (here refers to the total number of requests from mobile users), and the number of system warm-up contents to \(10^{4}\) before the experiment starts. Setting a higher preheating value will help to obtain more stable results in subsequent experiments. We assume that users follow a Poisson distribution, and assume that user requests satisfy the Zipf popularity distribution. We set the ratio of the cache space to the total content size to be 0.04. We set the parameter skewness (\(\alpha\)) of the Zipf popularity distribution to have a range of [0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2]. We set the user request rate in the low liquidity period to 10, and we set the range of user request rate in high liquidity period to [100, 200, 400, 600, 800, 1000]. Each simulation is carried out five times, and the result is the average of the five experiments. In our experimental scenario, in order to verify the scalability of PSBC in different network topologies, we chose the following real network topologies: GEANT (European Academic Network) and WIDE (Japanese Academic Network). The topology is shown in Figure 5.

### Simulation Result

The quality of service (QoS) is very important in computer network research, which is mainly reflected in the latency. Latency is the average time for the recorded request content to finally reach the user terminal, and it is also one of the key performance indicators for evaluating a caching scheme. The total latency includes queuing latency, processing latency, and propagation latency. The queuing latency and processing latency are negligible compared to the propagation latency, so we only consider the propagation latency.