Devising a method for the virtual clustering of the Internet of Things edge environment
DOI:
https://doi.org/10.15587/1729-4061.2024.298431Keywords:
Internet of Things, virtual cluster, edge environment, heterogeneity, fuzzy computing, balanceAbstract
The object of research is the process of load distribution in the edge environment of the Internet of Things.
The task to improve the efficiency of the functioning of the network of computing devices in the Internet of Things edge environment has been solved. Free resources of heterogeneous single-board computers were used to this end.
In the process of conducting research, an approach to the construction of an architecture for a virtual cluster of computers with limited resources was devised. The design took into account specific features of the edge environment on the Internet of Things. This has made it possible to propose a four-layer architecture instead of the standard seven-layer architecture of IoT sensor information processing device networks.
Stages in the virtual cluster construction in the edge environment on the Internet of Things were also defined. A three-stage procedure to form a virtual cluster was justified. This procedure made it possible to devise a method for the virtual clustering in the Internet of Things edge environment based on the proposed virtual cluster architecture.
The proposed method for building a virtual cluster in the Internet of Things edge environment was investigated. With a small network load, a virtual cluster has no advantage over a classic cluster. But with the growth of the network load, the virtual cluster prevails over the classic cluster in total performance; the advantage in total performance can exceed 10 %. It was also proven that for a heterogeneous environment, performance changes at full network load significantly depend on the number of virtual node groups. The research results on the method for building a virtual cluster in the Internet of Things edge environment can be explained by improving the balance of the network load at virtual clustering
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Copyright (c) 2024 Heorhii Kuchuk, Oleksandr Mozhaiev, Nina Kuchuk, Serhii Tiulieniev, Mykhailo Mozhaiev, Yurii Gnusov, Mykhailo Tsuranov, Tetiana Bykova, Sergii Klivets, Alexander Kuleshov
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