Dependency-Aware Dynamic Task Offloading Based on Deep Reinforcement Learning in Mobile-Edge Computing

The rapid advancement of mobile edge computing (MEC) networks has enabled the augmentation of the computational power of mobile devices (MDs) by offloading computationally intensive tasks to resource-rich edge nodes. This paper discusses the decision-making process for task offloading and resource allocation among multiple mobile devices connected to a base station. The primary objective is to minimize the time taken to complete tasks while simultaneously reducing energy consumption on the device under a time-varying wireless fading channel. This objective is formulated as an energy-efficiency cost (EEC) minimization problem, which cannot be solved by conventional methods. To address this challenge, we propose a dynamic offloading decision algorithm of dependent tasks (DODA-DT) that adjusts local task execution based on edge node status. The proposed algorithm facilitates fair competition among all devices for edge resources. Additionally, we use a deep reinforcement learning (DRL) algorithm based on an actor-critic learning structure to train the system to quickly identify near-optimal solutions. Numerical simulations demonstrate that the proposed algorithm effectively reduces the total cost of the task in comparison to previous algorithms.