This paper studies the new problem of how to dynamically allocate dependent tasks in resource-limited edge computing.

Abstract—In edge computing, an important problem is how to allocate dependent tasks to resource-limited edge servers, where some tasks can only be performed ...

Graph Convolutional Reinforcement Learning for Dependent Task Allocation in Edge Computing. IEEE ICA. Shiyao Ding; ,; Donghui Lin; ,; Xin Zhou. First page: 25 ...

Graph Convolutional Network Augmented Deep Reinforcement Learning for Dependent Task Offloading in Mobile Edge Computing ... Task Allocation for Multi-APs ...

This paper investigates the task graph offloading in MEC, considering the time-varying computation capabilities of edge computing devices.

Oct 27, 2022 · A multi-agent resource allocation algorithm based on graph convolution reinforcement learning which combines deep Q network (DQN) and graph attention network ...

Shiyao Ding, Donghui Lin and Xin Zhou for the paper entitled Graph Convolutional Reinforcement Learning for Dependent Task Allocation in Edge Computing. Best ...

Jun 7, 2024 · In this paper, we study the Dependent Task Offloading (DTO) problem within both single-user single-edge and multi-user multi-edge scenario.

Mar 30, 2023 · We firstly use Directed Acyclic Graph (DAG) to represent dependent task where nodes indicate subtasks and directed edges indicate dependencies ...

Graph convolutional network-based reinforcement learning for tasks offloading in multi-access edge computing ; Journal: Multimedia Tools and Applications, 2021, ...