Chainable Compute Analytics Container for Industrial Automation
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Solution Overview
Problem
Industrial automation systems face challenges in efficiently distributing computing operations across disparate resources due to insufficient compute resources on individual devices, necessitating a method to offload data processing tasks and manage analytics operations effectively.
Innovation Solution
A container orchestration system that identifies suitable computing resources, generates a distributed data processing flow, and deploys containers to perform analytics operations, allowing tasks to be chained across multiple devices and networks for efficient data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If containers are deployed to compute surface at or near industrial automation device, then data analysis capability is improved, but compute resource sufficiency deteriorates when functions need to be offloaded
Solution Approach 1:
The system segments data processing tasks into multiple independent containers that can be distributed across different compute surfaces. Each container handles a specific analytics function, allowing the workload to be divided and distributed rather than concentrated on a single compute surface, thereby resolving the contradiction between local analysis capability and resource sufficiency.
Solution Approach 2:
The invention transitions from a single-node compute model to a multi-node distributed compute model by introducing a compute orchestration layer. This dimensional shift enables containers to be deployed across multiple compute surfaces (on-premises and cloud), expanding the effective compute resource pool beyond the limitations of individual devices.
2Productivity
If distributed data processing flow is implemented across multiple devices, then analytics operation capability is improved, but system complexity increases
Solution Approach 1:
The compute orchestration system acts as an intermediary layer between the analytics application and the distributed compute surfaces. It manages container deployment, task distribution, and resource allocation automatically, shielding users from the underlying system complexity while enabling sophisticated distributed analytics operations.
Solution Approach 2:
The system implements self-service capabilities where the compute orchestration layer automatically provisions, deploys, and manages containers based on workload requirements. This eliminates the need for manual configuration and complex setup procedures, reducing operational complexity while maintaining advanced distributed processing capabilities.
3Productivity
If compute resources are offloaded to external systems, then processing capability is improved, but data transmission requirements increase
Solution Approach 1:
The system implements local quality by deploying containers directly on compute surfaces near the data source (industrial automation devices). This enables data processing to occur locally where it is generated, minimizing the need for data transmission to external systems while still allowing capability offloading when necessary through the distributed container architecture.
Data Source
AI summary
A method includes receiving (1) a request to perform of one or more analytics operations, and (2) data associated with an industrial automation system, wherein the industrial automation system comprises a plurality of devices configured to perform a plurality of automation operations, and wherein each of the plurality of devices comprises a compute surface configured to perform one or more software tasks, determining a plurality of data processing tasks included in the one or more analytics operations, identifying a portion of the plurality of devices to perform the plurality of data processing tasks based on the compute surface available for each of the plurality of devices, deploying one or more containers to each of the portion of the plurality of devices, and providing first instructions to a first container of the one or more containers to perform a first data processing task of the plurality of data processing tasks.