In mobile edge computing (MEC), edge caching (EC), i.e., deploying data closer to end users at the network edge layer, has become one of the most active research areas for mitigating the high latency caused by massive data in centralized storage systems. However, when caching data that are sensitive to freshness, distributed EC systems often face severe strong content consistency challenges, as such data undergo frequent updates and modifications over long usage cycles. To address the strong consistency issue faced by freshness-sensitive data in resource-constrained MEC scenarios, we innovatively reformulate this challenge as a content replacement problem. The objective is to minimize long-term content transmission costs while maintaining strong content consistency. More specifically, we propose the Content Strong Consistency Decision Transformer (CSC-DT) algorithm to maintain strong consistency. The cache replacement problem is modeled as a Markov Decision Process (MDP), with decisions constructed as trajectories. These trajectories are processed using the Transformer architecture to capture the long-term dependencies between the temporal dynamics of content requests and updates, optimizing cache replacement decisions. Validated with railway engineering data, which undergo frequent updates during the design phase, our method effectively maintains strong content consistency and reduces content transmission costs.

High-frequency, large-volume, and latency-sensitive multimedia data requests present challenges to the load capacity of edge caching-based content distribution networks. However, existing research primarily focuses on optimizing caching algorithms or employing centralized scheduling based on global system states, often overlooking content delivery and resource allocation tailored to request characteristics. In addition, centralized scheduling is vulnerable to network congestion and single points of failure, while distributed scheduling introduces high information synchronization overhead and difficulties in node collaboration. To address these issues, we propose a novel distributed cloud-edge request scheduling framework that decomposes the scheduling problem into multiple sub-problems for efficient algorithmic optimization. We propose DEPPO, which is an improved Multi-Agent Reinforcement Learning (MARL) algorithm designed to address the challenges of distributed scheduling while also tackling the issue of dimensional uncertainty in deep reinforcement learning(DRL). Experiments conducted on real-world datasets demonstrate that our approach significantly enhances request success rates and resource utilization.

The rapid expansion and intelligent development of railway infrastructure are driving significant growth in railway engineering data. For the dispersed users across railway networks’ complex topology, traditional centralized storage systems are insufficient for their low-latency, cost-efficient data retrieval. Existing edge caching solutions based on multi-agent reinforcement learning fail to address the asymmetric relationships among railway nodes, such as data centers, stations, and sections, etc. Besides, the complexity of computing Nash equilibrium points also gets higher as the number of agents (edge caching servers) increases. This study introduces a Hierarchical Game model-based MADRL-driven Collaborative Edge Caching method(HG-MCEC) tailored for railway engineering data. By considering the distribution characteristics and caching strategy games among railway nodes, a hierarchical game model for collaborative edge caching is constructed. This model treats the railway edge caching as a multi-agent system, in which each railway node server is regarded as an agent. HG-MCEC utilizes deep learning to mitigate computational complexity and recognize agents’ asymmetry. Upper-level agents adjust cache replacement strategies according to environmental changes and decisionmaking experience. Lower-level agents, under the guidance of upper-level decisions, optimize collaborative caching strategies toward achieving hierarchical game equilibrium. Using a highspeed railway building information modeling data for validation, the method significantly outperforms existing approaches by enhancing content hit rates and reducing latency at edge caching servers while decreasing system content transmission costs.