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Data Center Network” Related Articles

200 GE vs. 400 GE: Data Center Network Development Focus

Lu Hai 2020-06-17
The data center network, as the infrastructure for the development of Internet services, has moved from initial GE and 10GE networks to the 25G access + 100G interconnection phase.

Selecting Software or Hardware SDN for Automatic Data Center Network Deployment

Yang Xiaofeng 2020-06-17
SDN now has two possible development routes, with overlay control endpoints located either on physical switches or on virtual switches in servers.

The Technologies Behind the World's Fastest AI Cluster — Atlas 900

Liu Lifeng 2020-06-16
The intelligent lossless DCN built based on the world's highest-density 400G CloudEngine16800 not only meets the requirement of zero packet loss, but also supports large-scale 400GE network evolution.Huawei's intelligent lossless DCN achieves zero packet loss and equips Atlas 900 with the world's greatest computing power.

CloudFabric: Leading DCNs into the Intelligence Era

Leon Wang 2020-06-16
As a strategic highland for the digital economy to exert value, how to optimize the operation efficiency of the data center, more efficient release of computing power and the realization of data value have become the top priority.

CloudEngine 16800 Analysis 1: AI Chip + Algorithm Reaches 100% AI Computing Power

Zhang Yao 2019-01-18
CloudEngine 16800 is the industry’s first switch with an embedded high-performance AI chip that uses the innovative iLossless algorithm. It will redefine data center switches in the AI era.

CloudEngine 16800 Analysis 2: Three Futuristic Technologies Enable CloudEngine 16800’s Digital Flood Management

Zhang Yao 2019-01-23
Ultra-high-speed signal transmission, efficient power supply, and effective heat dissipation medium — three futuristic technologies that enable CloudEngine 16800 to manage digital floods.

CloudEngine 16800 Analysis 3: Distributed AI O&M, Acceleration toward the Automatic Driving Network

Zhang Yao 2019-01-23
FabricInsight, the network analyzer,leads to a distributed AI O&M architecture that significantly improves the flexibility and “deployability” of O&M systems, and greatly simplifies O&M.

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