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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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/21/9940 |
| _version_ | 1797512840195604480 |
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| author | Jack Marquez Oscar H. Mondragon Juan D. Gonzalez |
| author_facet | Jack Marquez Oscar H. Mondragon Juan D. Gonzalez |
| author_sort | Jack Marquez |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T06:07:12Z |
| format | Article |
| id | doaj.art-31176011bb444a559d10f51e2ab3462c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T06:07:12Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-31176011bb444a559d10f51e2ab3462c2023-11-22T20:25:24ZengMDPI AGApplied Sciences2076-34172021-10-011121994010.3390/app11219940An Intelligent Approach to Resource Allocation on Heterogeneous Cloud InfrastructuresJack Marquez0Oscar H. Mondragon1Juan D. Gonzalez2Automatics and Electronics Department, Universidad Autónoma de Occidente, Cali 760030, ColombiaAutomatics and Electronics Department, Universidad Autónoma de Occidente, Cali 760030, ColombiaAutomatics and Electronics Department, Universidad Autónoma de Occidente, Cali 760030, ColombiaCloud 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.https://www.mdpi.com/2076-3417/11/21/9940cloud computingresource allocationgenetic algorithm |
| spellingShingle | Jack Marquez Oscar H. Mondragon Juan D. Gonzalez An Intelligent Approach to Resource Allocation on Heterogeneous Cloud Infrastructures Applied Sciences cloud computing resource allocation genetic algorithm |
| title | 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_short | An Intelligent Approach to Resource Allocation on Heterogeneous Cloud Infrastructures |
| title_sort | intelligent approach to resource allocation on heterogeneous cloud infrastructures |
| topic | cloud computing resource allocation genetic algorithm |
| url | https://www.mdpi.com/2076-3417/11/21/9940 |
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