Containerized Edge Compute Units for Latency and Bandwidth Reduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing cloud computing architectures face challenges in latency, availability, bandwidth usage, data privacy, network security, and the capacity to process large volumes of data in real-time, particularly for applications like AI and ML workloads.
Innovation Solution
The use of containerized data center units that provide high-performance edge computing, enabling local processing and storage of data at the edge, reducing reliance on centralized data centers and improving latency and bandwidth efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If centralized data center processing is used, then data processing capacity is provided, but latency increases and real-time processing capability deteriorates
Solution Approach 1:
The patent segments the centralized data center into distributed edge computing nodes deployed at multiple locations. Each edge node independently processes data locally, eliminating the need for all data to traverse to a central location, thereby reducing latency while maintaining processing capacity through parallel distribution across nodes.
Solution Approach 2:
The patent transitions from a single-point centralized processing architecture to a multi-point distributed architecture, adding the spatial dimension of deployment locations. This dimensional change enables simultaneous access to processing capacity at multiple geographic points, reducing the effective distance and time for data processing operations.
2Power
If centralized data center processing is used, then data processing functions are provided, but network bandwidth consumption increases
Solution Approach 1:
The patent extracts data processing functions from the centralized data center and deploys them at edge locations near data sources. This extraction allows processing to occur locally, eliminating the need to transmit large volumes of data through the network to centralized facilities, thereby reducing bandwidth consumption while maintaining processing capabilities.
Solution Approach 2:
The patent implements processing quality and capacity locally at each edge node rather than centrally. Each edge node provides sufficient processing power for its local data processing needs, enabling autonomous operation and eliminating the bandwidth-intensive pattern of transmitting all data to a central location for processing.
3Power
If centralized data center processing is used, then data processing is performed, but data privacy and network security are compromised
Solution Approach 1:
The patent segments data processing operations into isolated edge nodes that handle data locally. This segmentation creates security boundaries that prevent data from traveling across public networks to centralized facilities, reducing exposure to network security threats while maintaining processing capability through distributed autonomous nodes.
Solution Approach 2:
The patent introduces edge nodes as intermediary components between data sources and centralized data centers. These intermediaries perform processing locally, acting as security buffers that prevent direct network transmission of sensitive data to centralized facilities, thereby enhancing data privacy and network security while preserving processing functions.
4Power
If centralized data center processing is used, then processing capacity is provided, but availability and reliability for real-time applications deteriorates
Solution Approach 1:
The patent divides the centralized processing capacity into multiple distributed edge nodes. This segmentation ensures that failures at one location do not affect others, improving overall system availability and reliability for real-time processing through redundant distributed architecture that maintains operational capacity across multiple independent points.
Solution Approach 2:
The patent provides processing capacity locally at each edge node with sufficient resources to handle real-time processing requirements autonomously. This local quality of processing eliminates dependency on centralized facilities for real-time operations, improving availability and reliability by enabling continuous local processing even when centralized systems are inaccessible or degraded.
Data Source
AI summary
A process can include receiving, by an edge compute unit, a pre-trained machine learning model from a cloud management platform, wherein the edge compute unit is deployed to an edge location and configured to obtain one or more sensor data streams at the edge location. The edge compute unit can transmit one or more batch uploads of information associated with inference performed by the edge compute unit using the pre-trained machine learning model and the one or more sensor data streams. The edge compute unit can receive one or more updated machine learning models generated by the cloud management platform responsive to the one or more batch uploads of information, wherein the one or more updated machine learning models are based on retraining or finetuning of the pre-trained machine learning model with the one or more batch uploads of information.