Graph Mapping Offloading Model Based On Deep Reinforcement Learning With Dependent Task

Abstract

In order to solve the problem of task offloading with dependent subtasks in mobile edge computing (MEC), we propose a graph mapping offloading model based on deep reinforcement learning (DRL). We model the user's computing task as directed acyclic graph (DAG), called DAG task. Then the DAG task is converted into a topological sequence composed of task vectors according to the custom priority. And the model we proposed can map the topological sequence to offloading decisions. The offloading problem is formulated as a Markov decision process (MDP) to minimize the tradeoff between latency and energy consumption. The evaluation results demonstrate that our DRL-based graph mapping offloading model has better decision-making ability, which proves the availability and effectiveness of the model.Index Terms-mobile edge computing (MEC), task offloading, directed acyclic graph (DAG), Markov decision process (MDP), deep reinforcement learning (DRL)

DRL-Based Dependent Task Offloading Strategies with Multi-Server Collaboration in Multi-Access Edge Computing

“…There is a lot of research on task offloading in MEC, which can be divided into three categories. First of all, for the dependent task offloading problem, some studies use the task call graph to model the complex dependencies between components in mobile applications [4,5], and some also use DAG [6][7][8] to represent the relationships between tasks. Specifically, Fan et al [4] converts the task decision problem of cost minimization into the shortest path problem, and uses the classical Lagrangian relaxation-based aggregate cost algorithm to approximate the problem.…”

“…Zhang et al [5] combined a set of fine-grained tasks to form a common topology, which expanded the tasks into a general task map. Mao et al [6] proposed a graph mapping task offloading model based on Deep Reinforcement Learning (DRL), which converts DAG tasks into topological sequences according to custom priorities and then maps them into offloading decisions. Chen et al [7] proposed ACED, a multidependent task computing offloading algorithm based on DAG, which is an actor-critic mechanism with two embedded layers.…”