Edge Fleet Management for AI Workload Latency
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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 requiring edge computing for AI and ML workloads.
Innovation Solution
The implementation of a fleet management system for edge compute units, which includes receiving monitoring information, status updates from connected edge assets, and user configuration inputs through a remote fleet management GUI. This system allows for the deployment of pre-configured edge compute units with ML/AI models and application software stacks, enabling local processing and reducing reliance on cloud infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is transmitted to centralized data centers for processing, then processing capacity is improved, but latency increases and bandwidth usage increases
Solution Approach 1:
The patent segments the centralized processing architecture into distributed edge computing nodes deployed throughout the network. These edge nodes process data locally near the data sources, eliminating the need to transmit all data to centralized data centers. This segmentation resolves the contradiction by maintaining processing capacity through distributed computation while reducing latency through local processing.
Solution Approach 2:
The patent introduces a new spatial dimension to the processing architecture by deploying edge compute units at multiple geographic locations throughout the network infrastructure. This transforms the single-point centralized processing model into a multi-dimensional distributed network, allowing data to be processed at the nearest edge node rather than requiring transmission to a remote centralized center, thus reducing latency while maintaining processing capacity.
2Productivity
If data is transmitted to centralized data centers for processing, then processing capacity is improved, but bandwidth usage increases
Solution Approach 1:
By segmenting the processing workload across distributed edge nodes, the patent eliminates the need to transmit large volumes of data across the network to centralized data centers. Each edge node processes data locally, dramatically reducing bandwidth consumption while maintaining overall system processing capacity through the collective capability of the distributed network.
Solution Approach 2:
The patent extracts the processing function from centralized data centers and places it at the network edge where data is generated. This extraction of processing capability to the data source location eliminates unnecessary data transmission, thereby reducing bandwidth usage while preserving processing capacity at the distributed edge nodes.
3Productivity
If centralized processing is used, then processing capacity is improved, but data privacy and security are worsened
Solution Approach 1:
The patent segments data processing across multiple distributed edge nodes rather than consolidating it in centralized data centers. This segmentation ensures that sensitive data remains localized and is processed only where needed, reducing exposure to centralized security risks while maintaining processing capacity through the distributed network architecture.
Solution Approach 2:
The patent applies local quality by processing data at the network edge where it is generated, rather than transmitting it to centralized locations. This local processing approach enhances data privacy and security by minimizing data transmission and exposure, while each edge node maintains the processing capacity needed to handle local workloads independently.
Data Source
AI summary
A process can include receiving monitoring information associated with a machine learning (ML) or artificial intelligence (AI) workload implemented by an edge compute unit of a plurality of edge compute units. Status information corresponding to a plurality of connected edge assets can be received, the plurality of edge compute units and connected edge assets included in a fleet of edge devices. A remote fleet management graphical user interface (GUI) can display a portion of the monitoring or status information for a subset of the fleet of edge devices, based on a user selection input, and can receive a user configuration input indicative of an updated configuration for at least one workload corresponding to a pre-trained ML or AI model deployed on the at least one edge compute unit. A cloud computing environment can transmit control information corresponding to the updated configuration to the at least one edge compute unit.