An Intelligent Scheduling Optimization Algorithm for Multimodal Cache Resources with Status Awareness of Metropolitan Area Network CDN Nodes

To address the resource scheduling challenges faced by metropolitan area network content delivery networks (CDN) when carrying multimodal traffic streams such as high-definition video, virtual reality (VR), and augmented reality (AR), this study proposes an intelligent optimization algorithm for multimodal cache resource scheduling that is CDN Node State Awareness. First, to address the dynamic nature of network topology and the heterogeneity of service streams, we construct a directed graph-based metropolitan area network CDN model. This model enables real-time perception of multi-dimensional states of nodes, including CPU utilization, memory usage, remaining bandwidth, and cache occupancy. We also introduce a mechanism for quantifying the transmission demand weight and cache value of multimodal content, providing a foundational support for differentiated scheduling. Second, at the optimization enhancement layer, we design a transmission path selection strategy, a cache replacement mechanism that integrates content value and access popularity, and an adaptive scheduling structure based on node load balancing. Furthermore, a Deep Q-Network is introduced at the cloud computing decision layer. Node states and user request features are modeled as a state space, while cache placement and request allocation strategies are modeled as an action space. A multi-objective reward function integrating hit rate, response latency, and packet loss rate is designed to achieve dynamic and intelligent scheduling of multimodal cache resources. Integrating path selection, cache updates, and fault recovery mechanisms to construct an overall optimization model enhances the system's adaptive scheduling capability in complex business services. The experiment shows that the algorithm has significant advantages in multi node collaborative scheduling: within 1-8 seconds, the transmission rate of node B reaches 30Mbps and the resource utilization rate of node A is improved; The resource download time remains stable at 4.4-4.9 seconds during 24-hour operation; In the large-scale scenario of 500 user requests, cross node cache load adaptive balancing, system overhead linearly increases, and data transmission security rate reaches 99.45%, creating an efficient, reliable, and scalable intelligent scheduling system for multi-mode content distribution in metropolitan area networks.

@article{Jiang2026,
  title     = {An Intelligent Scheduling Optimization Algorithm for Multimodal Cache Resources with Status Awareness of Metropolitan Area Network CDN Nodes},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {6},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Ruirong Jiang and Zhibiao Xiong and Junliang Wu and Jinyong Xu},
  doi       = {10.14569/IJACSA.2026.0170622},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170622}
}