Edge Container Orchestration for Industrial Automation Data Routing
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Solution Overview
Problem
Existing industrial automation systems underutilize data generated by devices and lack efficient methods to integrate software containers for enhancing functionality and data analysis.
Innovation Solution
Implementing a container orchestration system that bridges the operational technology (OT) and information technology (IT) environments, allowing for dynamic routing and edge orchestration to identify and deploy container images and firmware updates based on device characteristics and data types, using a container agent to determine and send appropriate solutions to edge devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a container orchestration system is implemented to integrate OT and IT environments, then functionality and data analysis capability are enhanced, but system complexity increases
Solution Approach 1:
The patent introduces a container orchestration system as an intermediary layer between OT and IT environments. This mediator translates device characteristics and data types from OT into container deployment configurations for IT, enabling integration without direct complex interactions between the two environments. The orchestration system acts as a buffer that manages the complexity internally while presenting simplified interfaces to both OT devices and IT resources.
Solution Approach 2:
The container orchestration system performs multiple functions simultaneously: it discovers OT devices, analyzes their characteristics, selects appropriate container images, manages firmware updates, and orchestrates deployment. This multi-functional approach consolidates what would otherwise require separate systems into a single platform, enhancing versatility while managing overall system complexity through consolidation.
2Productivity
If dynamic routing and edge orchestration are used to deploy container images based on device characteristics, then operational efficiency improves, but the complexity of device discovery and matching increases
Solution Approach 1:
The system performs preliminary actions by pre-discovering and cataloging OT devices with their characteristics before container deployment. It pre-analyzes device compatibility with available container images and pre-configures deployment mappings. This advance preparation enables rapid container deployment without real-time complex analysis, improving operational efficiency while managing discovery complexity through upfront work.
Solution Approach 2:
The container orchestration system implements feedback mechanisms where deployment outcomes are monitored and used to refine future device-container matching. The system learns from successful and unsuccessful deployments, adjusting its device characteristic analysis and container selection algorithms. This feedback loop improves operational efficiency over time while systematically reducing matching complexity through accumulated knowledge.
3Adaptability or versatility
If container images and firmware updates are sent to edge devices based on identified requirements, then system capability is expanded, but data transmission and deployment time increases
Solution Approach 1:
The system performs preliminary actions by pre-preparing and staging container images and firmware updates before they are needed at edge devices. It pre-validates compatibility and pre-configures deployment packages. This advance preparation reduces actual deployment time while maintaining the ability to expand system capability with appropriate container and firmware selections.
Solution Approach 2:
The container orchestration system implements dynamic deployment strategies where container images and firmware updates are sent only when actually needed based on real-time device requirements and conditions. Rather than continuous or periodic updates, the system dynamically determines deployment timing and targets, reducing unnecessary data transmission while maintaining system capability expansion where and when it is most beneficial.
Data Source
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AI summary
A non-transitory computer-readable medium includes instructions that, when executed by processing circuitry, cause the processing circuitry to receive, from an edge device of an industrial automation system, data indicative of a type of one or more devices in the industrial automation system, one or more types of data generated by the one or more devices, one or more components included in the one or more devices, firmware of the one or more devices, or any combination thereof. When executed, the instructions also cause the processing circuitry to identify, based on the received data, either a firmware update for the industrial automation system or, from a container repository, a container that is implementable on the edge device. Additionally, when executed, the instructions cause the processing circuitry to cause a container image for the container or firmware update for the firmware to be sent to the edge device.