## 3 Related Work  
Based on previous studies on resource allocation in ICNs, we can see a strong similarity between these strategies and content placement strategies. Arguably, the first and best known resource allocation strategy is Leave Copy Every Were (LCE) [3], where resources are shared and cached in all nodes in the return path. There are also other strategies proposed in this area, which can be divided into two categories. Strategies that allocate resources over the entire network or path, and strategies that allocate resources over the node itself. The first category refers to caching strategies that use coordination between nodes to allocate resources fairly, or to allocate popular resources to nodes that are considered important in the network. In [7], the authors propose a strategy called ProbCache, which approximates the capacity of paths to cache content using the hop count. In this strategy, nodes connected near the content source will be given priority to cache content over other nodes along the path. In [8], the authors propose a strategy that combines routing and resource allocation in the NDN network. This strategy uses a Q-learning algorithm based on the basic components of a semi-Markov decision process called SMDP to prove the best routing and resource allocation path. In [9], the authors propose a joint routing strategy with a resource allocation algorithm for wireless environments, which they called dynamic routing and resource allocation. In [10], the authors propose a resource allocation strategy for a limited network environment. They formulate an optimization problem that attempts to maximize the caching of popular content to reduce latency. At the same time, they minimize the hit variance to achieve a balanced resource allocation among the network nodes. In [11], the authors attempt to divide the network topology into different tiers based on the demands from different interfaces and video applications. The goal is to minimize redundancy and cache popular content closer to the consumers. In [12], the authors studied the object allocation problem using game theory as a distributed many-to-one in ISP networks to improve cache utilization in NDN. In [13], the authors measure the node utilization ratio (NUR), which considers the consumers distribution, the server distribution, and the content routing paths. Then they take the proportion between the NUR and the cache size of the node, along with the transmission ratio of the node, to allocate more contents in the relevant nodes. Despite the importance of these strategies, they do not offer much to the limited network community, as most of these strategies only rely on the popularity of the data in the network distribution. Further, they do not provide an effective solution for controlling the data that should be cached. Figure 1: NDN background. Regarding the second category of proposed strategies, we find that researchers have been more interested in classifying the data to give each class a chance to obtain space to cache its data. The main reason is that sharing data in caches by prioritizing caching for the most popular data prevents the rest of the data from having a chance to be cached. In [14], the authors propose a method for dynamic allocation of cache space. They divide the content into five categories based on the packet characteristics, where each category has a different priority to decide how many items should be cached in each category. In [15], the authors propose a caching strategy called Cache Based on Popularity and Class (CAPIC). This strategy caches a copy of the data one hop closer to the consumer for each request. In addition, when the CS of a node is full, the router checks the class of the received data and replaces it in the portion of the same class. Each class has a portion that changes periodically depending on the calculated popularity of the classes.