## 2 Background and Related Work
### Researches in Caching Field
#### 2.2.2 Cache Size Allocation Scheme  
Cache size allocation determines the efficiency of many cache studies, and researchers have done a lot on it. Homogeneous cache allocation is the default cache size allocation scheme in ICN, distributing the total cache budget equally to all nodes. Many other studies in ICNs, such as cache placement, replacement, and routing forwarding, are designed based on the assumption of homogeneous cache allocation [23; 24]. That scheme is simple and easy, but ignores the differences in the importance of nodes in content distribution, resulting in an insignificant effect. To solve the limitations of homogeneous cache allocation schemes, relevant researchers have proposed some improvement strategies. These studies are divided into two main categories. One is proportional allocation. Such studies typically analyzed the importance of nodes and then allocated the size of the cache proportional to the importance. For example, reference [25] considered such indicators as degree centrality, pressure centrality, betweenness centrality, proximity centrality, and graph centrality to design different cache size allocation schemes. Reference [26] used Manifold Learning to analyze network traffic characteristics and user behavior, classified routers according to their role in content distribution, and assigned cache sizes to nodes according to their categories. However, this scheme relied on complex automatic learning algorithms, and it was difficult to distinguish the importance of cache nodes in a new CCN environment. Reference [27] defined a new measure, request impact degree (RID), that reflected the importance of routers on the delivery path of content requests. Then they proposed a scheme to allocate the size of cache space in proportion to RID. Reference [28] analyzed how to allocate storage space for routers in a given storage budget network. By establishing a mathematical model to solve the problem, they found that network topology, network size, content popularity, and other factors would have some impact on cache allocation, so there was no one strategy for all cases. Reference [29] was the first study on cache size allocation, but it was designed for traditional networks with the purpose of selecting the appropriate cache location to reduce network traffic. Nevertheless, this also provided a solid foundation for future researches on cache space placement in ICN. Some studies considered that network structure had distinct community characteristics and the cache on nodes mainly satisfied local user's demand for content. Using the characteristic spectrum of network adjacency matrix to reflect the number of communities in the network [30], the researchers proposed that the importance of the node community should be used to represent the local importance of the node, and then determined the size of the node cache space in ICN. There are some other studies that considered both the network topology information and the dynamic changes of user demand. A new measure called node weight was defined, and a cache size allocation scheme based on node weight was designed. On the other hand, researchers designed optimization problems for some network performance, and continuously simplified and solved them. Reference [31] established an optimization model to study the migration of ICN under budget constraints from an economic perspective. There were also studies established three optimization models from the perspectives of users, ISPs, and content providers. They solved the three optimization problems and intersected their solutions to determine the location selection of cache nodes. Reference [32] established an optimization problem to determine the optimal storage allocation and content placement in a hierarchical topology with total storage budget constraints. The scheme also considered capacity constraints for downlinks and uplinksused to allocate storage to minimize data transmission costs. This solution applied to both wireless and wired networks.