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北京交通大学电子信息工程学院,北京 100044
Received:03 June 2024,
Revised:2024-08-29,
Published:25 March 2025
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陈佳, 刘上, 郜帅, 等. 面向全息通信的智算融合网络[J]. 电子学报, 2025, 53(03): 754-764.
CHEN Jia, LIU Shang, GAO Shuai, et al. Smart Computing Integration Network for Holographic Communication[J]. Acta Electronica Sinica, 2025, 53(03): 754-764.
陈佳, 刘上, 郜帅, 等. 面向全息通信的智算融合网络[J]. 电子学报, 2025, 53(03): 754-764. DOI:10.12263/DZXB.20240512
CHEN Jia, LIU Shang, GAO Shuai, et al. Smart Computing Integration Network for Holographic Communication[J]. Acta Electronica Sinica, 2025, 53(03): 754-764. DOI:10.12263/DZXB.20240512
随着全息技术的发展,全息通信在教育、娱乐和医疗等多个领域展现出广泛的应用前景.然而,现有的网络资源调度方法难以满足全息通信大带宽、低时延和数据同步的传输需求.智算融合网络具有原生的网内计算(in-network computing)能力,可以在数据包传输中对数据进行计算和优化,从而减少传输时延和网络带宽压力.本文根据前期的智融标识网络技术,研究面向全息通信的智算融合网络,提出基于业务标识、资源链标识、网络功能标识和网络组件标识的标识映射方法,实现全息业务到计算、存储和转发等多样化资源的自适应调度.基于标识映射方法,本文进一步设计了全息通信智算融合网络系统.实验结果表明,与传统边缘计算方法相比,面向全息通信的智算融合网络能够降低全息通信业务60%的网络带宽压力和45%的传输时延,同时提供更灵活的网络资源调度能力.
With the development of holographic technology
holographic communication shows a wide range of application prospects in many fields such as education
entertainment and medical care. However
the existing network resource scheduling method is difficult to meet the transmission requirements of holographic communication with large bandwidth
low latency and data synchronization. Smart computing integration network has native in-network computing capability. This allows for computation and optimization of data during packet transmission
thereby reducing transmission latency and network bandwidth pressure. Extends the work from the initial Smart Integration Identifier Network technology
exploring a smart computing integration network for holographic communication. It introduces an identifier mapping method based on service identifiers
resource chain identifiers
network function identifiers
and network component identifiers
enabling adaptive scheduling of holographic services to diverse resources such as computing
storage
and forwarding. Based on the identifier mapping method
this paper further designs a holographic communication smart computing integration network system. Experimental results show that
compared to traditional edge computing methods
the smart computing integration network can reduce the network bandwidth pressure by 60% and transmission latency by 45% for holographic communication services
while also offering more flexible network resource scheduling capabilities.
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BEHAVIORAL MODEL bmv 2 ) [EB/OL ] . ( 2022-02-11 [ 2024-06-03 ] . https://github.com/p4lang/behavioral-model https://github.com/p4lang/behavioral-model .
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