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PREDICTING THE NETWORK SERVICE QUALITY VIA THE LOG OF HARDWARE USAGE
- Authors: Grusho A.A.1, Zabezhailo M.I.1, Piskovski V.O.1,2, Timonina E.E.1
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Affiliations:
- Federal Research Center “Informatics and Management” RAS
- Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University
- Issue: No 3 (2025)
- Pages: 91-98
- Section: ARTIFICIAL INTELLIGENCE
- URL: https://ogarev-online.ru/0002-3388/article/view/304410
- DOI: https://doi.org/10.31857/S0002338825030098
- EDN: https://elibrary.ru/bgogdd
- ID: 304410
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Abstract
The costs of using a cloud computing infrastructure depend on its optimal configuration. The task is to reduce these costs and to support service quality at agreed level at the same time. To solve these tasks, we need methods for predicting the quality of the network service provided. Such predictions based on the logs of computing infrastructure usage, machine learning and methods for estimating service execution times are the subject of these work. These logs data are obtained through measurements and previously collected data on the operation of the telecommunications infrastructure. Measurements of infrastructure performance and service performance generate large amounts of data. The article discusses various methods for reducing dimensions and isolating significant variables in order to estimate discrepancies between target and predicted characteristics. In experiments, combining the model of a random forest with the method of reducing the dimensions via the principal component analysis has shown the best way.
About the authors
A. A. Grusho
Federal Research Center “Informatics and Management” RAS
Email: grusho@yandex.ru
Moscow, Russia
M. I. Zabezhailo
Federal Research Center “Informatics and Management” RAS
Email: m.zabezhailo@yandex.ru
Moscow, Russia
V. O. Piskovski
Federal Research Center “Informatics and Management” RAS; Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University
Email: vpiskovski@lvk.cs.msu.ru
Moscow, Russia; Moscow, Russia
E. E. Timonina
Federal Research Center “Informatics and Management” RAS
Email: eltimon@yandex.ru
Moscow, Russia
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