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

Hyunchan Park, Younghun Go, Kyungwoon Lee, Cheol Ho Hong

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number1484
JournalSensors
Volume23
Issue number3
DOIs
StatePublished - Feb 2023

Keywords

  • adaptive polling
  • edge computing
  • I/O virtualization
  • machine learning

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