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Resource-Aware Edge-Based Stream Analytics
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AI
DOI：10.1109/MIC.2022.3152478.png)
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Abstract
En 中文
Understanding how machine learning (ML) algorithms can be used for stream processing on edge devices remains an important challenge. Such ML algorithms can be represented as operators and dynamically adapted based on the resources on which they are hosted. Deploying ML algorithms on edge resources often focuses on carrying out inference on the edge, while learning and model development takes place on a cloud data center. In this article, we describe TinyMOA, a modified version of the open-source massive online analytics library for stream processing, that can be deployed across both local and remote edge resources using the Parsl and Kafka systems. Using an experimental testbed, we demonstrate how ML stream-processing operators can be configured based on the resource on which they are hosted, and discuss subsequent implications for edge-based stream-processing systems.
Keywords:
Machine learning algorithms
Heuristic algorithms
Cloud computing
Internet of Things
Machine learning
Data models
Computational modeling
edge computing
MOA
sensor data processing
stream processing
machine learning
Journal
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4.4
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2.0K
Citations:
2.0K