Dynamic performance-Energy tradeoff consolidation with contention-aware resource provisioning in containerized clouds

Containers have emerged as a more portable and efficient solution than virtual machines for cloud infrastructure providing both a flexible way to build and deploy applications. The quality of service, security, performance, energy consumption, among others, are essential aspects of their deployment,...

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Главные авторы:	Babenko, M. G., Бабенко, М. Г.
Формат:	Статья
Язык:	English
Опубликовано:	Public Library of Science 2022
Темы:	Dynamic performance Сontainerized clouds Job concentration paradigm
Online-ссылка:	https://dspace.ncfu.ru/handle/20.500.12258/18614
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id	ir-20.500.12258-18614
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spelling	ir-20.500.12258-186142025-07-11T10:24:50Z Dynamic performance-Energy tradeoff consolidation with contention-aware resource provisioning in containerized clouds Babenko, M. G. Бабенко, М. Г. Dynamic performance Сontainerized clouds Job concentration paradigm Containers have emerged as a more portable and efficient solution than virtual machines for cloud infrastructure providing both a flexible way to build and deploy applications. The quality of service, security, performance, energy consumption, among others, are essential aspects of their deployment, management, and orchestration. Inappropriate resource allocation can lead to resource contention, entailing reduced performance, poor energy efficiency, and other potentially damaging effects. In this paper, we present a set of online job allocation strategies to optimize quality of service, energy savings, and completion time, considering contention for shared on-chip resources. We consider the job allocation as the multilevel dynamic bin-packing problem that provides a lightweight runtime solution that minimizes contention and energy consumption while maximizing utilization. The proposed strategies are based on two and three levels of scheduling policies with container selection, capacity distribution, and contention-aware allocation. The energy model considers joint execution of applications of different types on shared resources generalized by the job concentration paradigm. We provide an experimental analysis of eighty-six scheduling heuristics with scientific workloads of memory and CPU-intensive jobs. The proposed techniques outperform classical solutions in terms of quality of service, energy savings, and completion time by 21.73-43.44%, 44.06-92.11%, and 16.38-24.17%, respectively, leading to a costefficient resource allocation for cloud infrastructures. 2022-02-01T07:39:56Z 2022-02-01T07:39:56Z 2022 Статья Canosa-Reyes R. M., Tchernykh A., Cortés-Mendoza J. M., Pulido-Gaytan B., Rivera-Rodriguez R., Lozano-Rizk J. E., Concepción-Morales E. R., Barrera H. E. C., Barrios-Hernandez C. J., Medrano-Jaimes F., Avetisyan A., Babenko, M. G. Dynamic performance-Energy tradeoff consolidation with contention-aware resource provisioning in containerized clouds // PLoS ONE. - 2022. - Том 17. - Выпуск 1 January. - Номер статьи e0261856. - DOI10.1371/journal.pone.0261856 http://hdl.handle.net/20.500.12258/18614 en PLoS ONE application/pdf Public Library of Science
institution	СКФУ
collection	Репозиторий
language	English
topic	Dynamic performance Сontainerized clouds Job concentration paradigm
spellingShingle	Dynamic performance Сontainerized clouds Job concentration paradigm Babenko, M. G. Бабенко, М. Г. Dynamic performance-Energy tradeoff consolidation with contention-aware resource provisioning in containerized clouds
description	Containers have emerged as a more portable and efficient solution than virtual machines for cloud infrastructure providing both a flexible way to build and deploy applications. The quality of service, security, performance, energy consumption, among others, are essential aspects of their deployment, management, and orchestration. Inappropriate resource allocation can lead to resource contention, entailing reduced performance, poor energy efficiency, and other potentially damaging effects. In this paper, we present a set of online job allocation strategies to optimize quality of service, energy savings, and completion time, considering contention for shared on-chip resources. We consider the job allocation as the multilevel dynamic bin-packing problem that provides a lightweight runtime solution that minimizes contention and energy consumption while maximizing utilization. The proposed strategies are based on two and three levels of scheduling policies with container selection, capacity distribution, and contention-aware allocation. The energy model considers joint execution of applications of different types on shared resources generalized by the job concentration paradigm. We provide an experimental analysis of eighty-six scheduling heuristics with scientific workloads of memory and CPU-intensive jobs. The proposed techniques outperform classical solutions in terms of quality of service, energy savings, and completion time by 21.73-43.44%, 44.06-92.11%, and 16.38-24.17%, respectively, leading to a costefficient resource allocation for cloud infrastructures.
format	Статья
author	Babenko, M. G. Бабенко, М. Г.
author_facet	Babenko, M. G. Бабенко, М. Г.
author_sort	Babenko, M. G.
title	Dynamic performance-Energy tradeoff consolidation with contention-aware resource provisioning in containerized clouds
title_short	Dynamic performance-Energy tradeoff consolidation with contention-aware resource provisioning in containerized clouds
title_full	Dynamic performance-Energy tradeoff consolidation with contention-aware resource provisioning in containerized clouds
title_fullStr	Dynamic performance-Energy tradeoff consolidation with contention-aware resource provisioning in containerized clouds
title_full_unstemmed	Dynamic performance-Energy tradeoff consolidation with contention-aware resource provisioning in containerized clouds
title_sort	dynamic performance-energy tradeoff consolidation with contention-aware resource provisioning in containerized clouds
publisher	Public Library of Science
publishDate	2022
url	https://dspace.ncfu.ru/handle/20.500.12258/18614
work_keys_str_mv	AT babenkomg dynamicperformanceenergytradeoffconsolidationwithcontentionawareresourceprovisioningincontainerizedclouds AT babenkomg dynamicperformanceenergytradeoffconsolidationwithcontentionawareresourceprovisioningincontainerizedclouds
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Dynamic performance-Energy tradeoff consolidation with contention-aware resource provisioning in containerized clouds

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