Proactive QoS-Aware Preemptive Resource Allocation in Mobile Edge Computing

Efficient Mobile Edge Computing (MEC) resource management is critical for diverse Quality of Service (QoS) demands, but traditional reactive methods and existing preemptive policies struggle in dynamic environments, causing suboptimal experiences. This paper proposes Proactive Adaptive Preemptive Allocation (PAPA), a novel framework for intelligent, forward-looking MEC resource management. PAPA features a QoS prediction module using lightweight sequence models to forecast short-term trends, assess risk, and trigger pre-warnings. Its core, the Proactive Preemptive Strategy Learning (APPL) module, employs a deep reinforcement learning (DRL) agent with a unique dual-layer reward. This includes a proactive penalty compelling anticipatory preemptive actions when predicted QoS enters a warning zone, differentiating it from reactive approaches. PAPA further enhances adaptability via meta-learning and dynamic priority mechanisms. Extensive simulations show PAPA consistently outperforms baselines, achieving superior throughput, reduced latency, and a significantly lower critical QoS violation rate than reactive DRL. Ablation studies confirm the impact of proactive penalty and meta-learning. PAPA demonstrates competitive energy efficiency and optimized preemption, affirming its robustness and practical viability in dynamic MEC environments.