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WISE-STACK Fully Integrated Edge Intelligence Private Cloud

Enjoy the Experience of a Public Cloud, with the Confidentiality of a Private Cloud

WISE-PaaS 3.0 On-Premise Deployment

WISE-STACK is Advantech’s private cloud offering with an on-premise deployment method. The fully integrated system offers the complete cloud experience and development toolset. End-to-end security features and a user defined access pathway ensures security and privacy of data and AI models. WISE-STACK is designed and tested to host AIoT applications with high availability, elastic expansion, and high data throughput characteristics. All WISE-PaaS services offer seamless integration with Advantech edge devices. Companies can easily complete their IoT initiatives by combining Advantech’s edge computing products with WISE-STACK.

System Architecture

private cloud architecture

Feature Highlights

The Benefits of WISE-STACK: Fast System Integration and High Data Throughput

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Validated performance, elastic expansion, and high availability for industrial AIoT

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Seamless integration with Advantech edge devices

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Complete data and AI model privacy

WISE-STACK Configuration

Advantech provides standardized, on-premise cloud configuration to satisfy large computation and high availability needs. Starting configuration consists of six server nodes and one optional analytic node with GPU.

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Controller Node: Controls operation and coordination of servers to achieve load balancing and high data throughput. Three controller nodes guarantee high availability for operational continuity, even when a server goes down. Users can perform hot-swap to maintain operation and ensure the performance of their private cloud.

HCI Node: Pure, hyper-converged node provides high computation power and storage space. The configuration allows businesses to setup their private clouds with a reduced number of servers in the initial stage.

Analytic Node: WISE-PaaS architecture incorporates Kubernetes – utilizing the power of NVDIA Tesla P4 graphic cards – to shorten the training period for deep model learning.