NetAP-ML: Machine Learning-Assisted Adaptive Polling Technique for Virtualized IoT Devices.
Sensors (Basel)
; 23(3)2023 Jan 29.
Article
em En
| MEDLINE
| ID: mdl-36772524
ABSTRACT
To maximize the performance of IoT devices in edge computing, an adaptive polling technique that efficiently and accurately searches for the workload-optimized polling interval is required. In this paper, we propose NetAP-ML, which utilizes a machine learning technique to shrink the search space for finding an optimal polling interval. NetAP-ML is able to minimize the performance degradation in the search process and find a more accurate polling interval with the random forest regression algorithm. We implement and evaluate NetAP-ML in a Linux system. Our experimental setup consists of a various number of virtual machines (2-4) and threads (1-5). We demonstrate that NetAP-ML provides up to 23% higher bandwidth than the state-of-the-art technique.
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MEDLINE
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En
Ano de publicação:
2023
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Article