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Multitask Offloading Strategy Optimization Based on Directed Acyclic Graphs for Edge Computing
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DOI：10.1109/JIOT.2021.3110412.png)
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Abstract
En 中文
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.
Keywords:
Dependent task offloading
directed acyclic graphs (DAGs)
graph convolutional neural network (GCN)
multi-access edge computing (MEC)
Journal
IF:
8.9
Papers:
1.4W
Citations:
7.8W