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    An Energy-Efficient In-Network Computing Paradigm for 6G

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    Date
    2021-12-01
    Author
    Hu, Ning
    Tian, Zhihong
    Du, Xiaojiang
    Guizani, Mohsen
    Metadata
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    Abstract
    In the 6G network era, energy-efficient computing means higher resource utilization and lower energy consumption. The traditional computing model regards the network as a transmission pipeline and has not fully explored the potential of network devices. In-network computing is a new type of computing model that delegates application-layer processing functions to the network data plane. The traffic can be processed during transmission, and the amount of traffic is reduced. As a result, the computing pressure and energy consumption of the cloud-side system is reduced. Most of the existing in-network computing models are based on programable network devices. Due to the limitations of the TCP/IP protocol, these methods can only handle application messages at the packet level. We propose an energy-efficient in-network computing paradigm for 6G that integrates network functions into a general computing platform instead of delegating computing tasks to network devices. The computing platform that integrates network functions replaces traditional network devices and acts as a network node. Unlike traditional network devices, the network node provides a unified operating environment for application tasks through hypervisors and containers. Therefore, data processing tasks can be deployed on cloud servers or on network nodes. The network controller schedules the processing tasks to execute on the network node passing the application traffic. The experimental results show that our paradigm can make full use of the computing resources of network nodes and significantly reduce the computing pressure, network transmission overhead and energy consumption of the data center.
    URI
    https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85111556077&origin=inward
    DOI/handle
    http://dx.doi.org/10.1109/TGCN.2021.3099804
    http://hdl.handle.net/10576/35497
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    • Computer Science & Engineering [‎2429‎ items ]

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