a 2-level distributed ppo scheduling approach for real-time heterogeneous mobile edge computing

Mobile edge computing (mec) provides low-latency computation for mobile devices, but efficient task scheduling remains a significant challenge due to user mobility and dynamic resource heterogeneity. existing deep reinforcement learning (drl) schedulers often lack a hierarchical structure that can adapt to both local and global system states. to bridge this gap, this paper introduces a novel 2-level hierarchical drl framework using proximal policy optimization (ppo). at the first level, a lightweight actor on each client device decides whether to execute a task locally or offload it, guided by a critic on the nearest edge node for rapid, localized adaptation. at the second level, an actor on each edge node, guided by a global critic in the cloud, manages inter-edge load balancing. for inter-edge offloading, a pareto-optimal selection mechanism is used to choose the destination. comprehensive simulation results demonstrate that our proposed framework significantly outperforms baseline methods, reducing average task latency by up to 60% and decreasing task failure rates by over 30%, providing a robust and scalable solution for dynamic scheduling in real-world mec environments.