电子学报2025，Vol.53Issue(6)：1771-1783,13.DOI:10.12263/DZXB.20251106
基于有向图与卷积网络强化学习的端侧协同算力资源分配方法
Directed Graph and Convolutional Network Reinforcement Learning for Terminal-Side Collaborative Computing Resource Allocation Scheme
顾健华 1冯建华 2许辉阳 3刘佟佟 3周婷3
作者信息
- 1. 清华大学计算机科学与技术系,北京 100084||中国移动通信集团终端有限公司,北京 100053
- 2. 清华大学计算机科学与技术系,北京 100084
- 3. 中国移动通信集团终端有限公司,北京 100053
- 折叠
摘要
Abstract
Driven by the concentrated surge of AI application scenarios,the increasing requirements on data commu-nication and computation in mobile applications is growing,the traditional cloud computing which relies on remote process-ing,often fails to meet low-latency requirements.Therefore,a new paradigm has emerged:terminal-side computing power that aggregate the vast terminal devices(including computing,storage,communication,etc)through distributed collabora-tion to efficiently execute computational tasks.However,constrained by the limited resource of standalone device and pro-hibitive communication overhead that impairs task coordination,such terminals still face significant challenges in achieving efficient collaboration for highly complex computing tasks.This paper presents device-to-device(D2D)communication as-sisted terminal devices collaborative computing,and a multi-agent soft actor-critic(MA-SAC)based on directed graph con-volutional network(DGCN)is designed to solve this problem.The subtasks included in directed acyclic graph(DAG)tasks were deployed to multiple terminals for collaborative computing,it is introduced to cater to the exigencies of task transmis-sion between disparate nodes within the DAG,and reduces the communication overhead when data transmission in the net-work.Through the simulations,the efficacy of the proposed scheme is demonstrated.The proposed scheme reduces network communication overhead by 38.2％and effectively improve resource utilization by 31.9％.关键词
端侧算力/终端协同/多跳D2D/端算力分配/有向图卷积网络Key words
terminal-side computing power/terminal collaboration/multi hop D2D/terminal-side computing power allocation/directed graph convolutional network分类
信息技术与安全科学引用本文复制引用
顾健华,冯建华,许辉阳,刘佟佟,周婷..基于有向图与卷积网络强化学习的端侧协同算力资源分配方法[J].电子学报,2025,53(6):1771-1783,13.
