时间触发系统的GNN链路预测

Carlos Lua, Ye Zhang, Omar Hekal, Daniel Onwuchekwa, R. Obermaisser
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引用次数: 0

摘要

近年来,图神经网络(GNNs)的研究日益受到关注。GNN被认为是解决需要处理不规则拓扑(如图数据)的机器学习任务的强大工具。同时,时间触发系统的调度问题的解决一直是人们争论的焦点。尽管提出了几种算法来解决这个问题,但没有一个考虑部分或全部利用GNN来解决时间触发调度问题。在这项工作中,我们提出了一种使用GNN进行时间触发系统动态自适应的方法。通过将作业分配问题转化为链路预测任务,我们使用gnn来解决时间触发系统的调度问题。初步结果表明,gnn在时间触发系统中执行任务分配问题具有很大的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
GNN Link Prediction for Time-Triggered Systems
Research on graph neural networks (GNNs) has increasingly gained popularity recently. GNN is considered a powerful tool for solving machine learning tasks that require dealing with irregular topologies such as graph data. Meanwhile, solving the scheduling problems for time-triggered systems has been debated for a long time. Even though several algorithms were proposed to solve this problem, none considered exploiting GNN partially or wholly, solving time-triggered scheduling. In this work, we propose an approach for dynamic adaptation in time-triggered systems using GNN. We use GNNs to solve scheduling problems for time-triggered systems by transforming job allocation probelms to link prediction tasks. The preliminary results show that GNNs have a promising potential to perform job allocation problems in time-triggered systems.
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