Task Offloading and Resource Optimization Based on Dependency-Aware Graph and Collaborative Deep Reinforcement Learning in Mobile Edge Computing

In mobile edge computing (MEC), computation offloading serves as an effective solution to bridge the gap between the stringent latency requirements of computational tasks and the limited processing capabilities of terminal devices (TDs). However, complex inter-task dependencies, dynamic network conditions, and the decentralized architecture of MEC systems pose significant challenges to efficient and adaptive task offloading. To address these challenges, this paper investigates dependency-aware task offloading and resource optimization in MEC environments. First, we propose a task feature extraction method based on dependency-aware graph neural networks (FEDG), which captures the hierarchical structure and varying importance of subtask dependencies by adaptively learning the aggregation weights of predecessor nodes and edges. Then, to address the joint dependency-aware task offloading and resource allocation problem under partial observability in MEC networks, we design a Dependency-aware Graph-based Multi-Agent deep reinforcement learning (DGMA) algorithm. DGMA integrates adaptive prioritized experience replay and correlation-based selective parameter sharing to improve learning efficiency and accelerate convergence in multi-agent environments. Extensive simulations demonstrate that DGMA achieves superior performance in terms of delay, energy consumption, offloading utility, and deadline violation rate.