Comprehensive Framework for Facilitating the Deployment of Distributed On-Premise Analytics Applications in Resource-Constraint Environments

Data Analytics (DA) and Machine Learning (ML) algorithms today are inside many applications and systems. They provide state-of-the-art anomaly detection, pattern recognition, health monitoring, predictive maintenance, or help to optimize and calibrate parameter combinations. However, they usually re...

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Bibliographic Details
Main Authors: Schoch, Nicolai, Becker, Pascal, Ashiwal, Virendra, Habib, Andrew
Format: Conference Proceeding
Language:English
Subjects:
Anomaly detection
Cloud computing
Distributed analytics
industrial communication technologies
Inference algorithms
Machine learning algorithms
machine learning deployment
Marine vehicles
Monitoring
on-premise deployment
Pattern analysis
Prediction algorithms
Predictive maintenance
Real-time systems
resource-constraint analytics
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Summary:Data Analytics (DA) and Machine Learning (ML) algorithms today are inside many applications and systems. They provide state-of-the-art anomaly detection, pattern recognition, health monitoring, predictive maintenance, or help to optimize and calibrate parameter combinations. However, they usually require strong compute resources. It is therefore a challenge to deploy the corresponding algorithms on edge devices or in embedded systems, such as on cloud-decoupled drives and drive systems, as these usually have strict resource constraints. In this work, we look at the deployment of DA/ML algorithms on combinations of resource-constraint edge and device environments. We present our framework for the structured and guided definition, setup, distribution, and deployment of such DA/ML algorithms. A guided workflow leads a user through algorithm specification and configuration. While doing so, it extracts information, which is necessary for the distribution and deployment of the DA/ML algorithm to a suitable device-edge combination. With this solution, a DA/ML algorithm can be executed for sensor data processing and, in the ML context, for inference, on device-edge combinations without permanent cloud connection, e.g., on a drive/gateway combination on a cruise ship, so that it can be used for real-time anomaly detection or pattern analysis.
ISSN:2161-8089
DOI:10.1109/CASE59546.2024.10711682