Adaptive Data Prefetching for Real-Time Analytics: A Deep Reinforcement Learning Approach

With the rapid advancement of information technology and artificial intelligence, the continual generation of massive data has led to emerging demands on the quality of service for real-time processing of large-scale data. Distributed computing pushes storage and computation closer to end devices, cutting transmission delays and balancing resources with proximity, making it a key enabler for large-scale real-time analytics. Yet exponential device growth and ballooning data volumes have created storage and communication bottlenecks at computing nodes. This paper focuses on a scenario where computing nodes connect with multiple data source nodes for real-time analysis of large-scale data. By constructing a mathematical model, we formulate the problem as an optimization task aimed at minimizing average task completion time under resource, latency, and sequence constraints. We prove it is NP-hard and faces coupled resource decisions and dynamic networks. To tackle these issues, we propose a data prefetching strategy that integrates online learning prediction with a Deep Q-Network (DQN). The online learning algorithm predicts transmission rates to provide future state information to the DQN agent. Based on the system model, we design the state, action space, and reward function within the reinforcement learning framework, leveraging optimization mechanisms like experience replay and double networks. Experiments conducted in a simulated complex computing node environment demonstrate that, compared to baseline strategies, our approach reduces average task completion time, enhances system resource utilization and service quality, and exhibits strong adaptability to fluctuating network conditions.