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Huawei Cloud introduces “1+3+M+N” global automotive cloud infrastructure layout

Gabriella From Gasgoo| November 21 , 2022 14:57 BJT

Beijing (Gasgoo)- Huawei Cloud introduced its “1+3+M+N” global automotive cloud infrastructure layout at a event held on November 18 to facilitate the development of intelligent connected vehicles.

Huawei Cloud introduces “1+3+M+N” global automotive cloud infrastructure layout

Photo credit: Huawei

Specifically, the “1” means that Huawei adopts one automotive storage and computing network worldwide, namely, KooVerse. The network is a distributed cloud infrastructure with global coverage and a unified architecture. It realizes a 30ms delay coverage circle to meet the different delay requirements of enterprise services. Currently, KooVerse is available in 75 zones in 29 geographic regions, serving customers in more than 170 countries and regions.

The “3” represents Huawei Cloud’s three ultra-large data centers located in Ulanqab, Gui’an, and Wuhu, with dedicated, green, safe, and compliance automotive zones. 

The “M” signifies Huawei Cloud’s many distributed V2X nodes. The company has cooperated with several communications operators like China Unicom, building multiple distributed V2X nodes for high-efficiency cloud service in Beijing, Shanghai, Guangzhou, and more. 

Lastly, the “N” indicates the over 50 automotive-use data access points that Huawei Cloud plans to build across China, ensuring the nonstop 24-hours cloud access for autonomous driving R&D data.

At the event, Huawei Cloud also launched several innovative solutions to support the digitalization and intelligence upgrade of the auto industry. 

In the autonomous driving field, Huawei Cloud, together with its partners in the industry, provide end-to-end autonomous driving R&D solutions for automakers and autonomous driving algorithm companies, to support the business closed-loop of autonomous driving data.

As for AI algorithm training, Huawei Cloud’s AI development line, ModelArts, supports end-to-end training efficiency and distributed multi-stage caching that reduces training time by 50%. For large-scale cluster training, the company’s topology-aware scheduling and dynamic routing software technology can improve the training performance by 30%.

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