GCN and DRL based on dependent task offloading mechanism in edge computing

Task offloading is critical for optimizing resource allocation in edge computing systems. In practical scenarios, user applications often comprise multiple interdependent tasks, where both task dependencies and parallelism strongly affect offloading decisions. This paper presents a novel dependent task offloading framework for multi-edge server environments. The task offloading problem is formulated as a Markov Decision Process(MDP) to minimize computational delay. Task dependencies are modeled using a Directed Acyclic Graph(DAG), and a Graph Convolutional Network(GCN) encoder is employed to extract DAG features as inputs for a Deep Reinforcement Learning(DRL) model. The proposed DRL-based method applies the Proximal Policy Optimization(PPO) algorithm to simultaneously select subtasks and determine their offloading decisions. Experimental evaluations across varying numbers of subtasks confirm the effectiveness of the approach, demonstrating superior performance compared to state-of-the-art solutions.