NetAP-ML: Machine Learning-Assisted Adaptive Polling Technique for Virtualized IoT Devices

Autor:	Hyunchan Park, Younghun Go, Kyungwoon Lee, Cheol-Ho Hong
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	edge computing I/O virtualization adaptive polling machine learning Chemical technology TP1-1185
Zdroj:	Sensors, Vol 23, Iss 3, p 1484 (2023)
Druh dokumentu:	article
ISSN:	23031484 1424-8220
DOI:	10.3390/s23031484
Popis:	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.
Databáze:	Directory of Open Access Journals
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