Multitask Offloading Strategy Optimization Based on Directed Acyclic Graphs for Edge Computing

被引:69
|
作者
Chen, Jiawen [1 ]
Yang, Yajun [1 ]
Wang, Chenyang [1 ]
Zhang, Heng [1 ]
Qiu, Chao [1 ]
Wang, Xiaofei [1 ]
机构
[1] Tianjin Univ, Coll Intelligence & Comp, Tianjin 300072, Peoples R China
基金
美国国家科学基金会; 中国博士后科学基金;
关键词
Dependent task offloading; directed acyclic graphs (DAGs); graph convolutional neural network (GCN); multi-access edge computing (MEC); IOT; ALLOCATION;
D O I
10.1109/JIOT.2021.3110412
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
With the advancement of the user application service demands, the IoT system tends to offload the tasks to the edge server for execution. Most of the current studies on edge computation offloading ignore the dependencies between components of the application. The few pieces of research on edge computing offloading which focus on the topology of application are primarily applied in single-user scenarios. Unlike previous work, our work mainly solves dependent task offloading with edge computing in multiuser scenarios, which is more in line with reality. In this article, the dependent task offloading problem is modeled as a Markov decision process (MDP) first. Then, we propose an actor-critic mechanism with two embedding layers for directed acyclic graphs (DAGs)-based multiple dependent tasks computation offloading, namely, ACED, by jointly considering the topology of the application and the channel interference between several users. Finally, the results of simulations also show the priorities of the proposed ACED algorithm.
引用
收藏
页码:9367 / 9378
页数:12
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