Hierarchical Container Deployment for Low-Latency OT Data Analysis
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Solution Overview
Problem
Industrial control systems in OT networks face communication latency issues when collecting and analyzing data, particularly at high speeds or frequencies, leading to inefficiencies in controlling industrial devices due to time delays in data transmission and processing.
Innovation Solution
A container orchestration system dynamically deploys containers across different hierarchical levels of an industrial automation system to collect and analyze data, prioritizing placement near data sources for granular analysis, and manages resource allocation and scheduling to minimize latency and optimize data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If data is collected and transmitted long distances within the OT network for processing and analysis, then centralized data processing is achieved, but communication latency increases
Solution Approach 1:
The patent segments the centralized processing architecture by deploying containerized data processing functions to multiple hierarchical levels within the OT network. Containers are distributed to edge devices, control systems, and enterprise systems, allowing data processing to occur locally at the source rather than requiring long-distance transmission to a single centralized location. This segmentation reduces communication latency while maintaining automated processing capabilities.
Solution Approach 2:
The patent introduces a hierarchical dimension to the data processing architecture, organizing containers across multiple levels (edge, control, enterprise) rather than a single flat centralized structure. This dimensional change allows data to be processed at the appropriate hierarchical level closest to its source, reducing transmission distances and latency while preserving centralized coordination through the orchestration system.
2Productivity
If containers are deployed to multiple hierarchical levels, then data processing efficiency is improved, but system complexity increases
Solution Approach 1:
The patent employs universal containerized applications that can execute identical data processing functions across multiple hierarchical levels. The same container image can be deployed to edge devices, control systems, and enterprise systems, providing multi-functionality and reducing complexity through standardization. This universality allows efficient data processing at each level without requiring level-specific customizations.
Solution Approach 2:
The container orchestration system acts as an intermediary that manages the complexity of deploying and coordinating containers across hierarchical levels. The orchestrator handles container provisioning, scheduling, and communication coordination, shielding individual system components from the complexity of multi-level deployment while enabling efficient distributed data processing.
3Loss of time
If containers are dynamically deployed near data sources, then data transmission latency is reduced, but resource allocation complexity increases
Solution Approach 1:
The patent implements dynamic container deployment where containers are automatically provisioned and moved to the most appropriate hierarchical level based on real-time data source locations and processing requirements. The orchestration system continuously monitors system state and dynamically adjusts container placement to minimize transmission latency while managing resource allocation through automated scheduling algorithms.
Solution Approach 2:
The container orchestration system provides self-service resource allocation by automatically selecting target devices and hierarchical levels for container deployment based on predefined policies and real-time system conditions. The system autonomously manages resource provisioning and container placement without manual intervention, reducing the operational complexity of resource allocation while achieving optimal data transmission efficiency.
Data Source
AI summary
Systems and methods described herein may relate to a system that includes one or more industrial devices that perform one or more operations within an industrial automation system. One or more industrial devices may include a compute surface able to perform one or more software tasks. The system may include a processor that determines a trigger event has occurred. The processor may determine additional data and a target device based on the trigger event, where the processor may be located on a different hierarchical level as compared to the target device. The processor may determine a container to be deployed to the target device based on the container generating the additional data when deployed on the target device. The processor may deploy the container to the target device.