An Intelligent Approach to Resource Allocation on Heterogeneous Cloud Infrastructures

Cloud computing systems are rapidly evolving toward multicloud architectures supported on heterogeneous hardware. Cloud service providers are widely offering different types of storage infrastructures and multi-NUMA architecture servers. Existing cloud resource allocation solutions do not comprehens...

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Autores principales:	Jack Marquez, Oscar H. Mondragon, Juan D. Gonzalez
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	cloud computing resource allocation genetic algorithm Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/31176011bb444a559d10f51e2ab3462c
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id	oai:doaj.org-article:31176011bb444a559d10f51e2ab3462c
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spelling	oai:doaj.org-article:31176011bb444a559d10f51e2ab3462c2021-11-11T15:02:11ZAn Intelligent Approach to Resource Allocation on Heterogeneous Cloud Infrastructures10.3390/app112199402076-3417https://doaj.org/article/31176011bb444a559d10f51e2ab3462c2021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/9940https://doaj.org/toc/2076-3417Cloud computing systems are rapidly evolving toward multicloud architectures supported on heterogeneous hardware. Cloud service providers are widely offering different types of storage infrastructures and multi-NUMA architecture servers. Existing cloud resource allocation solutions do not comprehensively consider this heterogeneous infrastructure. In this study, we present a novel approach comprised of a hierarchical framework based on genetic programming to solve problems related to data placement and virtual machine allocation for analytics applications running on heterogeneous hardware with a variety of storage types and nonuniform memory access. Our approach optimizes data placement using the Hadoop File System on heterogeneous storage devices on multicloud systems. It guarantees the efficient allocation of virtual machines on physical machines with multiple NUMA (nonuniform memory access) domains by minimizing contention between workloads. We prove that our solutions for data placement and virtual machine allocation outperform other state-of-the-art approaches.Jack MarquezOscar H. MondragonJuan D. GonzalezMDPI AGarticlecloud computingresource allocationgenetic algorithmTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 9940, p 9940 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	cloud computing resource allocation genetic algorithm Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	cloud computing resource allocation genetic algorithm Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Jack Marquez Oscar H. Mondragon Juan D. Gonzalez An Intelligent Approach to Resource Allocation on Heterogeneous Cloud Infrastructures
description	Cloud computing systems are rapidly evolving toward multicloud architectures supported on heterogeneous hardware. Cloud service providers are widely offering different types of storage infrastructures and multi-NUMA architecture servers. Existing cloud resource allocation solutions do not comprehensively consider this heterogeneous infrastructure. In this study, we present a novel approach comprised of a hierarchical framework based on genetic programming to solve problems related to data placement and virtual machine allocation for analytics applications running on heterogeneous hardware with a variety of storage types and nonuniform memory access. Our approach optimizes data placement using the Hadoop File System on heterogeneous storage devices on multicloud systems. It guarantees the efficient allocation of virtual machines on physical machines with multiple NUMA (nonuniform memory access) domains by minimizing contention between workloads. We prove that our solutions for data placement and virtual machine allocation outperform other state-of-the-art approaches.
format	article
author	Jack Marquez Oscar H. Mondragon Juan D. Gonzalez
author_facet	Jack Marquez Oscar H. Mondragon Juan D. Gonzalez
author_sort	Jack Marquez
title	An Intelligent Approach to Resource Allocation on Heterogeneous Cloud Infrastructures
title_short	An Intelligent Approach to Resource Allocation on Heterogeneous Cloud Infrastructures
title_full	An Intelligent Approach to Resource Allocation on Heterogeneous Cloud Infrastructures
title_fullStr	An Intelligent Approach to Resource Allocation on Heterogeneous Cloud Infrastructures
title_full_unstemmed	An Intelligent Approach to Resource Allocation on Heterogeneous Cloud Infrastructures
title_sort	intelligent approach to resource allocation on heterogeneous cloud infrastructures
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/31176011bb444a559d10f51e2ab3462c
work_keys_str_mv	AT jackmarquez anintelligentapproachtoresourceallocationonheterogeneouscloudinfrastructures AT oscarhmondragon anintelligentapproachtoresourceallocationonheterogeneouscloudinfrastructures AT juandgonzalez anintelligentapproachtoresourceallocationonheterogeneouscloudinfrastructures AT jackmarquez intelligentapproachtoresourceallocationonheterogeneouscloudinfrastructures AT oscarhmondragon intelligentapproachtoresourceallocationonheterogeneouscloudinfrastructures AT juandgonzalez intelligentapproachtoresourceallocationonheterogeneouscloudinfrastructures
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An Intelligent Approach to Resource Allocation on Heterogeneous Cloud Infrastructures

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