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Using the machine learning methods for resource management of high availability broadcasting containerized system

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dc.contributor.authorAminova, A.
dc.contributor.authorOrlov, A.
dc.contributor.authorRovnyagin, M.
dc.contributor.authorGuminskaia, A.
dc.contributor.authorChernilin, F.
dc.contributor.authorHrapov, A.
dc.contributor.authorРовнягин, Михаил Михайлович
dc.contributor.authorХрапов, Александр Сергеевич
dc.date.accessioned2024-11-26T13:24:49Z
dc.date.available2024-11-26T13:24:49Z
dc.date.issued2020
dc.description.abstract© 2020 The Authors. Published by Elsevier B.V.Most of today applications are built on a micro-service architecture, where a large application is divided into different functional parts that can be deployed on many containers that enable good load balancing. Container management tools need system load forecasting means to timely balance system load. It is an important problem for systems with direct streams of popular events which periodically have large splashes of a load. In this paper, we propose the load prediction method for such systems in two cases: Usual and broadcasting workload. Also, we propose an architecture of adaptive infrastructure using our load forecasting method.
dc.format.extentС. 773-779
dc.identifier.citationUsing the machine learning methods for resource management of high availability broadcasting containerized system / Aminova, A. [et al.] // Procedia Computer Science. - 2020. - 169. - P. 773-779. - 10.1016/j.procs.2020.02.166
dc.identifier.doi10.1016/j.procs.2020.02.166
dc.identifier.urihttps://www.doi.org/10.1016/j.procs.2020.02.166
dc.identifier.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85084445566&origin=resultslist
dc.identifier.urihttps://openrepository.mephi.ru/handle/123456789/21770
dc.relation.ispartofProcedia Computer Science
dc.titleUsing the machine learning methods for resource management of high availability broadcasting containerized system
dc.typeConference Paper
dspace.entity.typePublication
oaire.citation.volume169
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