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

Mao, N, Chen, Y, Guizani, M and Lee, GM Graph Mapping Offloading Model Based on Deep Reinforcement Learning With Dependent Task. In: 17th Int. Wireless Communications & Mobile Computing Conference - IWCMC 2021, 28 June 2021 - 02 July 2021, Harbin, China. (Accepted)

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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.

Item Type: Conference or Workshop Item (Paper)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Computer Science & Mathematics
Date Deposited: 24 May 2021 11:21
Last Modified: 03 Sep 2021 23:17
URI: https://researchonline.ljmu.ac.uk/id/eprint/15054

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