{"title":"基于深度双向LSTM的数据中心网络流量预测与资源分配","authors":"Yonghuai Wang","doi":"10.1109/ICCCI51764.2021.9486790","DOIUrl":null,"url":null,"abstract":"This article first proposes an adaptive traffic scheduling strategy for optoelectronic hybrid data centers. The strategy is composed of a deep bidirectional LSTM-based traffic prediction model and a prediction-assisted traffic scheduling method. The simulation results confirm that the presented method can achieve non-congested intra-data center traffic scheduling and higher network performance even under heavy traffic conditions.","PeriodicalId":180004,"journal":{"name":"2021 3rd International Conference on Computer Communication and the Internet (ICCCI)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Traffic Prediction and Resource Allocation Based on Deep Bidirectional LSTM in Data Center Networks\",\"authors\":\"Yonghuai Wang\",\"doi\":\"10.1109/ICCCI51764.2021.9486790\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article first proposes an adaptive traffic scheduling strategy for optoelectronic hybrid data centers. The strategy is composed of a deep bidirectional LSTM-based traffic prediction model and a prediction-assisted traffic scheduling method. The simulation results confirm that the presented method can achieve non-congested intra-data center traffic scheduling and higher network performance even under heavy traffic conditions.\",\"PeriodicalId\":180004,\"journal\":{\"name\":\"2021 3rd International Conference on Computer Communication and the Internet (ICCCI)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-06-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 3rd International Conference on Computer Communication and the Internet (ICCCI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCCI51764.2021.9486790\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 3rd International Conference on Computer Communication and the Internet (ICCCI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCCI51764.2021.9486790","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Traffic Prediction and Resource Allocation Based on Deep Bidirectional LSTM in Data Center Networks
This article first proposes an adaptive traffic scheduling strategy for optoelectronic hybrid data centers. The strategy is composed of a deep bidirectional LSTM-based traffic prediction model and a prediction-assisted traffic scheduling method. The simulation results confirm that the presented method can achieve non-congested intra-data center traffic scheduling and higher network performance even under heavy traffic conditions.