Cloud Micro-LLMs for Resource-Limited CPE Network Extension
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Solution Overview
Problem
Customer premises equipment (CPE) with limited computing resources struggle to run advanced applications and access hardware interfaces due to network isolation and resource constraints, limiting the functionality of IoT devices.
Innovation Solution
Extending customer premises networks onto cloud provider networks using edge CPE devices as gateways, enabling tunneling and secure remote access, and hosting micro-large language models (LLMs) in the cloud to offload computing tasks and enhance device functionality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If CPE devices are equipped with advanced computing resources to run sophisticated applications and access hardware interfaces, then device functionality and AI capabilities are improved, but device complexity and power consumption increase
Solution Approach 1:
The patent extracts complex AI processing and application execution from the CPE device to cloud-based infrastructure. Virtual machines and containers are deployed on cloud servers, allowing sophisticated applications and hardware interface access to run remotely while the CPE device maintains a lightweight client, thereby improving functionality without increasing local device complexity
Solution Approach 2:
The patent introduces cloud-based virtualization infrastructure as an intermediary between the CPE device and hardware interfaces. Virtual machines and containers act as mediators that provide advanced computing capabilities and hardware access through network connections, enabling the CPE device to leverage external resources without direct hardware integration
2Adaptability or versatility
If CPE devices are equipped with advanced computing resources to run sophisticated applications and access hardware interfaces, then device functionality and AI capabilities are improved, but power consumption increases
Solution Approach 1:
The patent extracts computationally intensive AI processing and application execution from the CPE device to cloud-based infrastructure. Virtual machines and containers are deployed on cloud servers with high-performance hardware, allowing sophisticated applications to run remotely while the CPE device maintains a lightweight client, thereby improving functionality without increasing local power consumption
Solution Approach 2:
The cloud-based virtualization infrastructure provides self-service capabilities where virtual machines and containers automatically manage resource allocation and execution. The CPE device simply connects to these pre-configured services, eliminating the need for local hardware upgrades and reducing power consumption while maintaining access to advanced AI capabilities
3Speed
If applications are deployed locally on CPE devices, then application performance is improved, but device complexity and resource requirements increase
Solution Approach 1:
The patent extracts application execution from local CPE devices to cloud-based virtual machines. Containers are deployed on remote servers, allowing applications to run in isolated environments with dedicated resources while the CPE device maintains a lightweight client connection, improving performance without increasing local device complexity
Solution Approach 2:
The patent uses virtualization to create virtual copies of computing environments on cloud infrastructure. Virtual machines replicate the functionality of physical servers, allowing applications to be copied and deployed across multiple cloud instances while the CPE device accesses these replicated services remotely, maintaining performance without local hardware complexity
Data Source
AI summary
Disclosed are various embodiments that enhance customer premises device functionality through the use of cloud-based micro-large language models. In one embodiment, a layer-3 virtual private network is established between a cloud provider network and a customer premises network of a customer. A layer-2 virtual interface is established for a cloud-based artificial intelligence (AI) engine executed on the cloud provider network using a tunnel to encapsulate layer-2 traffic over the layer-3 virtual private network. The cloud-based AI engine is used to provide a functionality for an edge device on the customer premises network.