Shiwen Song , Qinghua Tian , Xiao Zhang , Xiangjun Xin , Fu Wang , Dandan Sun , Xiongyan Tang , Lei Zhu , Feng Tian , Sitong Zhou , Qi Zhang
{"title":"无源光网络中基于 GRU 流量预测的带宽分配方案","authors":"Shiwen Song , Qinghua Tian , Xiao Zhang , Xiangjun Xin , Fu Wang , Dandan Sun , Xiongyan Tang , Lei Zhu , Feng Tian , Sitong Zhou , Qi Zhang","doi":"10.1016/j.optcom.2024.131222","DOIUrl":null,"url":null,"abstract":"<div><div>With the advancement of information technology, network slicing technology has emerged as a viable solution for ensuring Quality of Service (QoS) in optical access networks. Current research is increasingly focusing on the integration of optical access networks with network slicing technologies. This paper proposes a bandwidth allocation scheme based on traffic prediction, specifically designed for resource management in optical network slicing scenarios. The scheme employs a Gated Recurrent Unit (GRU) neural network model to forecast future traffic, and combines bandwidth and latency factors to allocate bandwidth to each slice based on predicted values, thereby meeting the QoS requirements of various services. Simulation results indicate that, compared to the baseline algorithm, the proposed scheme achieved a 35.42% increase in Explaining Variance Score (EVS) and a 38.16% improvement in R2 score for factory slicing prediction. Similarly, for data center slicing prediction, the EVS score increased by 32.29% and the R2 score improved by 41.96%. In terms of performance metrics such as latency, packet loss rate, and throughput, the proposed algorithm outperforms both traditional prediction algorithms and the baseline algorithm.</div></div>","PeriodicalId":19586,"journal":{"name":"Optics Communications","volume":null,"pages":null},"PeriodicalIF":2.2000,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A bandwidth allocation scheme based on GRU traffic prediction in passive optical networks\",\"authors\":\"Shiwen Song , Qinghua Tian , Xiao Zhang , Xiangjun Xin , Fu Wang , Dandan Sun , Xiongyan Tang , Lei Zhu , Feng Tian , Sitong Zhou , Qi Zhang\",\"doi\":\"10.1016/j.optcom.2024.131222\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>With the advancement of information technology, network slicing technology has emerged as a viable solution for ensuring Quality of Service (QoS) in optical access networks. Current research is increasingly focusing on the integration of optical access networks with network slicing technologies. This paper proposes a bandwidth allocation scheme based on traffic prediction, specifically designed for resource management in optical network slicing scenarios. The scheme employs a Gated Recurrent Unit (GRU) neural network model to forecast future traffic, and combines bandwidth and latency factors to allocate bandwidth to each slice based on predicted values, thereby meeting the QoS requirements of various services. Simulation results indicate that, compared to the baseline algorithm, the proposed scheme achieved a 35.42% increase in Explaining Variance Score (EVS) and a 38.16% improvement in R2 score for factory slicing prediction. Similarly, for data center slicing prediction, the EVS score increased by 32.29% and the R2 score improved by 41.96%. In terms of performance metrics such as latency, packet loss rate, and throughput, the proposed algorithm outperforms both traditional prediction algorithms and the baseline algorithm.</div></div>\",\"PeriodicalId\":19586,\"journal\":{\"name\":\"Optics Communications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2024-10-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Optics Communications\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0030401824009593\",\"RegionNum\":3,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"OPTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Optics Communications","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0030401824009593","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"OPTICS","Score":null,"Total":0}
A bandwidth allocation scheme based on GRU traffic prediction in passive optical networks
With the advancement of information technology, network slicing technology has emerged as a viable solution for ensuring Quality of Service (QoS) in optical access networks. Current research is increasingly focusing on the integration of optical access networks with network slicing technologies. This paper proposes a bandwidth allocation scheme based on traffic prediction, specifically designed for resource management in optical network slicing scenarios. The scheme employs a Gated Recurrent Unit (GRU) neural network model to forecast future traffic, and combines bandwidth and latency factors to allocate bandwidth to each slice based on predicted values, thereby meeting the QoS requirements of various services. Simulation results indicate that, compared to the baseline algorithm, the proposed scheme achieved a 35.42% increase in Explaining Variance Score (EVS) and a 38.16% improvement in R2 score for factory slicing prediction. Similarly, for data center slicing prediction, the EVS score increased by 32.29% and the R2 score improved by 41.96%. In terms of performance metrics such as latency, packet loss rate, and throughput, the proposed algorithm outperforms both traditional prediction algorithms and the baseline algorithm.
期刊介绍:
Optics Communications invites original and timely contributions containing new results in various fields of optics and photonics. The journal considers theoretical and experimental research in areas ranging from the fundamental properties of light to technological applications. Topics covered include classical and quantum optics, optical physics and light-matter interactions, lasers, imaging, guided-wave optics and optical information processing. Manuscripts should offer clear evidence of novelty and significance. Papers concentrating on mathematical and computational issues, with limited connection to optics, are not suitable for publication in the Journal. Similarly, small technical advances, or papers concerned only with engineering applications or issues of materials science fall outside the journal scope.