Edge Computing Appliance Latency Reduction via Local Container Orchestration
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Solution Overview
Problem
Cloud-based edge computing implementations face limitations in latency and resource efficiency, and not all devices can connect to cloud services, necessitating a local computing solution for lower latency and broader device compatibility.
Innovation Solution
An edge computing appliance provides on-premise computing resources, including graphics processing and storage, through virtual instances and containers, compatible with various devices, enabling real-time services like VR and AR without the need for extensive device capabilities, and allowing devices to access compute services with minimal configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If cloud-based edge computing implementations are used, then large-scale computing resources are available, but latency increases and response time deteriorates
Solution Approach 1:
The patent introduces an edge computing appliance as an intermediary device deployed between cloud infrastructure and end-user devices. This appliance provides local computing resources (CPU, GPU, storage) that mediate between cloud-based services and local devices, enabling low-latency processing while maintaining connection to cloud resources when needed. The appliance acts as a local proxy that can operate independently or in coordination with cloud services.
2Power
If cloud-based service connections are established, then computing power is improved, but device compatibility deteriorates for devices without connection capability
Solution Approach 1:
The edge computing appliance is designed with universal compatibility across multiple device types including smartphones, tablets, AR/VR headsets, and IoT devices. It provides standardized computing services through virtualized containers that can serve diverse applications (graphics processing, AI inference, video rendering) on various hardware platforms. The appliance's multi-functional design allows it to adapt to different device capabilities and requirements.
3Productivity
If on-device processing capabilities are enhanced, then application performance is improved, but device complexity and processing requirements increase
Solution Approach 1:
The patent extracts complex computing workloads from end-user devices and relocates them to the edge computing appliance. By offloading tasks such as graphics rendering, AI model inference, and video processing to the external appliance, the solution maintains high application performance while keeping local devices simple and lightweight. The appliance handles the computational burden locally, eliminating the need for devices to have extensive processing capabilities.
Data Source
AI summary
An edge computing device receives, from a user device via an isolated local area network, a request for computing services that are hosted on the edge computing device and not on the user device. The edge computing device accesses policies that are applicable to the user device and the requested computing services. Based on the policies and the requested computing services, the edge computing device instantiates a container configured to provide the requested computing services. The container receives offloaded processing tasks from the device. The container executes the offloaded processing tasks, and sends, to the user device, data indicative of the processed tasks.