Carlos Lua, Ye Zhang, Omar Hekal, Daniel Onwuchekwa, R. Obermaisser
{"title":"时间触发系统的GNN链路预测","authors":"Carlos Lua, Ye Zhang, Omar Hekal, Daniel Onwuchekwa, R. Obermaisser","doi":"10.1109/ICAIIC57133.2023.10066960","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"GNN Link Prediction for Time-Triggered Systems\",\"authors\":\"Carlos Lua, Ye Zhang, Omar Hekal, Daniel Onwuchekwa, R. Obermaisser\",\"doi\":\"10.1109/ICAIIC57133.2023.10066960\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":105769,\"journal\":{\"name\":\"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)\",\"volume\":\"33 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-02-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICAIIC57133.2023.10066960\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAIIC57133.2023.10066960","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.