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On ml methods for network powered by computing infrastructure
- Authors: Smeliansky R.L.1, Stepanov E.P.1
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Affiliations:
- Lomonosov Moscow State University
- Issue: Vol 516, No 1 (2024)
- Pages: 103-112
- Section: COMPUTER SCIENCE
- URL: https://journals.rcsi.science/2686-9543/article/view/265340
- DOI: https://doi.org/10.31857/S2686954324020176
- EDN: https://elibrary.ru/XHPDZH
- ID: 265340
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Abstract
The paper considers the application of machine learning methods for optimal resource management for Network Powered by Computing (NPC) – a new generation computing infrastructure. The relation between the proposed computing infrastructure and the GRID concept is considered. It is shown how machine learning methods applied to computing infrastructure management make it possible to solve the problems of computing infrastructure management that did not allow the GRID concept to be fully implemented. As an example, the application of multi-agent optimization methods with reinforcement learning for network resources management is considered. It is shown that the application of multi-agent machine learning methods makes it possible to increase the speed of distribution of transport flows and ensure optimal NPC network channel load according to the criterion of uniform load distribution, and that such management of network resources is more effective than a centralized approach.
About the authors
R. L. Smeliansky
Lomonosov Moscow State University
Author for correspondence.
Email: smel@cs.msu.su
Corresponding Member, Faculty of computational mathematics and cybernetics, Department of computing systems and automation
Russian Federation, MoscowE. P. Stepanov
Lomonosov Moscow State University
Email: estepanov@lvk.cs.msu.ru
Faculty of computational mathematics and cybernetics, Department of computing systems and automation
Russian Federation, MoscowReferences
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