# Intelligent Caching with Graph Neural Network-Based Deep Reinforcement Learning on SDN-Based ICN
## 2 Related Work  
Classical caching placement algorithms commonly used in the literature include LCE, LCD, PROB_CACHE, and CLAM [20; 21; 22; 23]. LCE involves copying content at any cache between the serving and receiving nodes, while LCD caches content in the immediate neighbourhood of the serving node in the receiver's direction. PROB_CACHE probabilistically caches content on a path, considering various factors. CL4M aims to place content in nodes with high graph-based centrality. In addition to placement algorithms, traditional caching replacement algorithms such as Least Recently Used (LRU), Least Frequently Used (LFU), and First-In-First-Out (FIFO) are commonly employed [24; 25]. LRU discards the least recently accessed content, LFU replaces the least frequently used content first, and FIFO evicts the first item inserted in the cache. However, these traditional algorithms are often considered inefficient, yielding poorer performance than deep learning-based caching algorithms. DRL-based caching algorithms have demonstrated remarkable achievements in recent years [26; 27]. In [5], the authors developed a deep Q-network (DQN)-based caching algorithm designed explicitly for mobile edge networks. The application of DRL in the Internet of Vehicles (IoV) field has also gained substantial attention. As the demand for computation and entertainment in autonomous driving and vehicular scenarios increases, researchers have been actively advancing caching strategies to enhance the user experience. In [28], the authors propose CoCaRL, a caching strategy leveraging DRL and a multi-level federated learning framework. They utilize a DDQN [14] to optimize the cache hit ratio of local roadside units (RSUs), neighbour RSUs, and cloud data centers in vehicular networks. Their approach also incorporates federated learning to enable decentralized model training. Another study [29] introduces a quality of experience (QoE)-driven RSU caching model based on DRL. Their caching algorithm addresses the growing demand for time-sensitive short videos in a 5G-based IoV scenario. The reward in their DRL model is defined as the ratio of the number of videos interesting to each user to the total number of videos stored in the RSU. Furthermore, in [6], the authors design a DQN-based strategy to optimize joint computing and edge caching in a three-layer IoV-ICN network architecture, encompassing vehicles, edges, and cloud layers. In [30], the authors proposed a social-aware vehicular edge computing architecture to efficiently deliver popular content to end-users in vehicular social networks. They introduce a social-aware graph pruning search algorithm to assign content consumer vehicles to the shortest path with the most relevant content providers. Additionally, they utilize a DRL method to optimize content distribution across the network. In [31], the authors develop an IoV-specific edge caching model that enables collaborative content caching among mobile vehicles and considers varying content popularity and channel conditions. Additionally, the framework empowers each vehicle agent to make caching decisions based on environmental observations autonomously. In [32], the authors proposed a spatial-temporal correlation approach to predict content popularity in the IoV. They introduce a DRL-based multi-agent caching strategy, where each RSU is an independent agent, to optimize caching decisions. In [33], the authors investigate joint computation offloading, data caching, transmission path selection, subchannel assignment, and caching management in the IoV-based environment. Dynamic online algorithms such as the Simulated Annealing Genetic Algorithm (SAGA) and DQN are adopted to minimize the content access latency. In addition to DRL, GNNs have emerged as another effective approach in addressing caching problems. One notable application of GNNs in caching is presented in [34]. The authors introduce a GNN-based caching algorithm to optimize the cache hit ratio in a named data networking (NDN) context. Their approach involves two key steps.