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Dependent Task Offloading in Edge Computing Using GNN and Deep Reinforcement Learning
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DOI：10.1109/JIOT.2024.3374969.png)
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Abstract
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Task offloading is a widely used technology in edge computing (EC), which declines the makespan of user task with the aid of resourceful edge servers. How to solve the competition for computation and communication resources among tasks is a fundamental issue in task offloading. Besides, real-life user tasks often comprise multiple interdependent subtasks. Dependencies among subtasks significantly raises the complexity of task offloading, and makes it difficult to propose generalized approaches for scenarios of different sizes. In this article, we study the dependent task offloading (DTO) problem within both single-user single-edge and multiuser multiedge scenario. First, we use directed acyclic graph (DAG) to model dependent task, where nodes and directed edges represent the subtasks and their interdependencies, respectively. Then, we propose a task scheduling method based on graph attention network (GAT) and deep reinforcement learning (DRL) to minimize the makespan of user tasks. More specifically, our method introduces a multidiscrete action DRL scheduler that simultaneously determines which subtask to consider and whether it should be offloaded at each step, and employs GAT to encode the graph-based state representation. To stabilize and speed up DRL scheduler training, we pretrain GAT encoder with unsupervised learning. Extensive experiments demonstrate that our proposed approach can be applied to various environments and outperforms prior methods.
Keywords:
Task analysis
Training
Heuristic algorithms
Cloud computing
Quality of experience
Scheduling
Program processors
Deep reinforcement learning (DRL)
directed acyclic graph (DAG)
edge computing (EC)
graph attention network (GAT)
task offloading
Journal
IF:
8.9
Papers:
1.4W
Citations:
7.8W
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