通过快速自适应带宽分配解决动态业务环境的概念漂移

Lihua Ruan, Elaine Wong
{"title":"通过快速自适应带宽分配解决动态业务环境的概念漂移","authors":"Lihua Ruan, Elaine Wong","doi":"10.1109/ICCCN58024.2023.10230123","DOIUrl":null,"url":null,"abstract":"Passive optical networks are envisioned to become increasingly complex as they support more and more diverse and immersive services that have different capacity, latency, and reliability needs. In the near term, they are expected to support the delivery of a diverse and immersive set of services including mixed reality, holographic communication, human-to-machine/robot communications, Tactile Internet, and digital sensing. However, in supporting these diverse and immersive services, traffic on the network will become increasingly dynamic across a range of different time scales. The upstream bandwidth in a passive optical network is typically shared by a group of end users, meaning that the uplink latency performance as experienced by each end user is thus highly dependent on the amount and when bandwidth to that end user is allocated. Machine learning enhanced bandwidth allocation algorithms have been proposed but are typically stationary, primarily-designed or pre-trained based on certain network configurations. In dynamic network conditions where traffic can evolve over time, concept drift, a phenomenon whereby the underlying distribution of the training data will no longer be representative of that in deployment, may occur. In view of future dynamic network conditions, we present a novel online reinforcement learning based bandwidth allocation scheme to address concept drift in machine learning enhanced passive optical network. The scheme facilitates self-adaptive decisions in real-time to accommodate dynamic network environments with varying traffic types and network loads. Results from comprehensive performance evaluation of the scheme show that rapid and self-adaptive bandwidth decisions can be achieved, yielding ~ 60% latency improvement in dynamic traffic environments.","PeriodicalId":132030,"journal":{"name":"2023 32nd International Conference on Computer Communications and Networks (ICCCN)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Addressing Concept Drift of Dynamic Traffic Environments through Rapid and Self-Adaptive Bandwidth Allocation\",\"authors\":\"Lihua Ruan, Elaine Wong\",\"doi\":\"10.1109/ICCCN58024.2023.10230123\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Passive optical networks are envisioned to become increasingly complex as they support more and more diverse and immersive services that have different capacity, latency, and reliability needs. In the near term, they are expected to support the delivery of a diverse and immersive set of services including mixed reality, holographic communication, human-to-machine/robot communications, Tactile Internet, and digital sensing. However, in supporting these diverse and immersive services, traffic on the network will become increasingly dynamic across a range of different time scales. The upstream bandwidth in a passive optical network is typically shared by a group of end users, meaning that the uplink latency performance as experienced by each end user is thus highly dependent on the amount and when bandwidth to that end user is allocated. Machine learning enhanced bandwidth allocation algorithms have been proposed but are typically stationary, primarily-designed or pre-trained based on certain network configurations. In dynamic network conditions where traffic can evolve over time, concept drift, a phenomenon whereby the underlying distribution of the training data will no longer be representative of that in deployment, may occur. In view of future dynamic network conditions, we present a novel online reinforcement learning based bandwidth allocation scheme to address concept drift in machine learning enhanced passive optical network. The scheme facilitates self-adaptive decisions in real-time to accommodate dynamic network environments with varying traffic types and network loads. Results from comprehensive performance evaluation of the scheme show that rapid and self-adaptive bandwidth decisions can be achieved, yielding ~ 60% latency improvement in dynamic traffic environments.\",\"PeriodicalId\":132030,\"journal\":{\"name\":\"2023 32nd International Conference on Computer Communications and Networks (ICCCN)\",\"volume\":\"100 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 32nd International Conference on Computer Communications and Networks (ICCCN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCCN58024.2023.10230123\",\"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 32nd International Conference on Computer Communications and Networks (ICCCN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCCN58024.2023.10230123","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

无源光网络将变得越来越复杂,因为它们支持越来越多样化和沉浸式的业务,这些业务具有不同的容量、延迟和可靠性需求。在短期内,它们有望支持多种沉浸式服务的交付,包括混合现实、全息通信、人机/机器人通信、触觉互联网和数字传感。然而,为了支持这些多样化的沉浸式服务,网络上的流量将在不同的时间尺度上变得越来越动态。无源光网络中的上行带宽通常由一组最终用户共享,这意味着每个最终用户所经历的上行延迟性能因此高度依赖于分配给该最终用户的带宽的数量和时间。已经提出了机器学习增强带宽分配算法,但通常是固定的,主要设计或基于某些网络配置预训练。在流量随时间变化的动态网络条件下,可能会出现概念漂移,即训练数据的底层分布将不再代表部署中的分布。针对未来动态网络环境,提出了一种基于在线强化学习的带宽分配方案,以解决机器学习增强型无源光网络中的概念漂移问题。该方案能够实时自适应决策,以适应不同流量类型和网络负载的动态网络环境。综合性能评估结果表明,该方案能够实现快速、自适应的带宽决策,在动态流量环境下时延提高约60%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Addressing Concept Drift of Dynamic Traffic Environments through Rapid and Self-Adaptive Bandwidth Allocation
Passive optical networks are envisioned to become increasingly complex as they support more and more diverse and immersive services that have different capacity, latency, and reliability needs. In the near term, they are expected to support the delivery of a diverse and immersive set of services including mixed reality, holographic communication, human-to-machine/robot communications, Tactile Internet, and digital sensing. However, in supporting these diverse and immersive services, traffic on the network will become increasingly dynamic across a range of different time scales. The upstream bandwidth in a passive optical network is typically shared by a group of end users, meaning that the uplink latency performance as experienced by each end user is thus highly dependent on the amount and when bandwidth to that end user is allocated. Machine learning enhanced bandwidth allocation algorithms have been proposed but are typically stationary, primarily-designed or pre-trained based on certain network configurations. In dynamic network conditions where traffic can evolve over time, concept drift, a phenomenon whereby the underlying distribution of the training data will no longer be representative of that in deployment, may occur. In view of future dynamic network conditions, we present a novel online reinforcement learning based bandwidth allocation scheme to address concept drift in machine learning enhanced passive optical network. The scheme facilitates self-adaptive decisions in real-time to accommodate dynamic network environments with varying traffic types and network loads. Results from comprehensive performance evaluation of the scheme show that rapid and self-adaptive bandwidth decisions can be achieved, yielding ~ 60% latency improvement in dynamic traffic environments.
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
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信