# Multi-Agent Deep-Q Network-Based Cache Replacement Policy for Content Delivery Networks
## 1 Introduction  
In the current age of digital advancement, the internet depends heavily on the utilization of content delivery networks (CDNs) to ensure efficient access to the enormous amount of content available to users across the globe. The increasing popularity of streaming services like Amazon Prime and Netflix, along with the widespread cloud and web services, has highlighted the importance of secure and efficient content delivery. CDNs are strategically deployed with an extensive network of servers, spanning various locations worldwide. The surge in massive content traffic and the high-quality service expectations of users have placed considerable strain on backbone networks. To meet these demands, edge caching servers are implemented to optimize the positions of CDN infrastructure, ensuring that these servers are in close proximity to users' networks. This approach aims to reduce the burden on the source CDN servers and enable efficient content delivery to users that improves scalability, enhances reliability, and reduces bandwidth usage, all of which contribute to a better user experience [1, 2, 3]. CDNs are essential for enhancing the performance, reliability, and security of websites by caching content at various locations globally. This helps reduce latency and improveload times for users regardless of their geographic location. Cache replacement algorithms play a vital role in CDNs by deciding which content stays in the cache memory and which is replaced, which significantly affects the cache hit ratio and the overall performance of CDNs. The primary goal of CDNs is to maximize the server's hit rate, which represents the proportion of requests served directly from the cache. The dynamic nature of caching environments makes cache replacement challenging, as these algorithms must effectively identify popular content and determine which content to replace. Effective cache replacement algorithms help optimize the performance of a CDN by ensuring that the most relevant and frequently accessed content remains readily available. The choice of algorithm can significantly impact the overall performance and cost-effectiveness of a CDN. Conventional approaches like least recently used (LRU), least frequently used (LFU), First-In-First-Out (FIFO), and Random Replacement (RR), along with their variations, offer scalability and ease of implementation. However, they often rely on heuristics and lack adaptability to diverse network configurations. Additionally, these strategies do not provide the flexibility to incorporate custom costs or utility functions. Furthermore, existing conventional algorithm models are designed for single-cache systems and do not account for cooperative caching scenarios [4; 5]. In recent years, many research fields have utilized machine learning-based approaches to solve the cache replacement problem. Researchers have investigated the use of different reinforcement learning methods to address cache replacement in networks [6; 7]. ML-based cache replacement solutions for CDNs face several challenges, including high computational overhead, significant data requirements, and the need for constant adaptation to dynamic content and user behavior. Additionally, single-agent approaches make decisions based on their own local data and experiences, which limits their effectiveness in the highly distributed and collaborative environment of CDNs. Multiple cache nodes should work together to optimize the overall performance of CDNs, and a single-agent system might not efficiently coordinate these nodes, potentially leading to suboptimal caching decisions and reduced hit rates. In the context of CDNs, the state space can become exceedingly large due to the vast amount of content, varied user behaviors, and dynamic network conditions. Managing this immense state space can be computationally intensive and memory-heavy, leading to scalability issues. As the number of states grows, the model becomes more complex and harder to manage, often resulting in slower learning rates and less effective performance.