深度学习在任务调度中的试验研究

2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM) Pub Date : 2019-08-01 DOI:10.1109/PACRIM47961.2019.8985079

Jumpei Kono, M. Kai

{"title":"深度学习在任务调度中的试验研究","authors":"Jumpei Kono, M. Kai","doi":"10.1109/PACRIM47961.2019.8985079","DOIUrl":null,"url":null,"abstract":"Task scheduling is one of the methods to minimize the processing time of a program in parallel processing. However, because task scheduling problems belong to strongly NP-hard combinatorial optimization problems [1], it is hard to find an optimal solution within a practical search time. In this research, we performed an experiment for speeding up the traditional search method based on the branch and bound algorithm by conducting scheduling with the use of deep learning.","PeriodicalId":152556,"journal":{"name":"2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Trial Experiment of Deep Learning For Task Scheduling\",\"authors\":\"Jumpei Kono, M. Kai\",\"doi\":\"10.1109/PACRIM47961.2019.8985079\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Task scheduling is one of the methods to minimize the processing time of a program in parallel processing. However, because task scheduling problems belong to strongly NP-hard combinatorial optimization problems [1], it is hard to find an optimal solution within a practical search time. In this research, we performed an experiment for speeding up the traditional search method based on the branch and bound algorithm by conducting scheduling with the use of deep learning.\",\"PeriodicalId\":152556,\"journal\":{\"name\":\"2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PACRIM47961.2019.8985079\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PACRIM47961.2019.8985079","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 0

摘要

任务调度是并行处理中使程序处理时间最小化的一种方法。然而，由于任务调度问题属于强NP-hard组合优化问题[1]，很难在实际搜索时间内找到最优解。在本研究中，我们利用深度学习进行调度，对传统的基于分支定界算法的搜索方法进行了提速实验。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

A Trial Experiment of Deep Learning For Task Scheduling

Task scheduling is one of the methods to minimize the processing time of a program in parallel processing. However, because task scheduling problems belong to strongly NP-hard combinatorial optimization problems [1], it is hard to find an optimal solution within a practical search time. In this research, we performed an experiment for speeding up the traditional search method based on the branch and bound algorithm by conducting scheduling with the use of deep learning.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)

自引率

0.00%

发文量