A multi‐objective method for virtual machines allocation in cloud data centres using an improved grey wolf optimization algorithm
Abstract Cloud computing is a rapidly evolving computational technology. It is a distributed computational system that offers dynamically scaled computational resources, such as processing power, storage, and applications, delivered as a service through the Internet. Virtual machines (VMs) allocatio...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-11-01
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| Series: | IET Communications |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cmu2.12274 |
| _version_ | 1798036007799488512 |
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| author | Masoud Hashemi Danial Javaheri Parisa Sabbagh Behdad Arandian Karlo Abnoosian |
| author_facet | Masoud Hashemi Danial Javaheri Parisa Sabbagh Behdad Arandian Karlo Abnoosian |
| author_sort | Masoud Hashemi |
| collection | DOAJ |
| description | Abstract Cloud computing is a rapidly evolving computational technology. It is a distributed computational system that offers dynamically scaled computational resources, such as processing power, storage, and applications, delivered as a service through the Internet. Virtual machines (VMs) allocation is known as one of the most significant problems in cloud computing. It aims to find a suitable location for VMs on physical machines (PMs) to attain predefined aims. So, the main purpose is to reduce energy consumption and improve resource utilization. Because the VM allocation issue is NP‐hard, meta‐heuristic and heuristic methods are frequently utilized to address it. This paper presents an energy‐aware VM allocation method using the improved grey wolf optimization (IGWO) algorithm. Our key goals are to decrease both energy consumption and allocation time. The simulation outcomes from the MATLAB simulator approve the excellence of the algorithm compared to previous works. |
| first_indexed | 2024-04-11T21:05:26Z |
| format | Article |
| id | doaj.art-f0837e8b5a8a45c1b7b4e7be32f65e47 |
| institution | Directory Open Access Journal |
| issn | 1751-8628 1751-8636 |
| language | English |
| last_indexed | 2024-04-11T21:05:26Z |
| publishDate | 2021-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Communications |
| spelling | doaj.art-f0837e8b5a8a45c1b7b4e7be32f65e472022-12-22T04:03:21ZengWileyIET Communications1751-86281751-86362021-11-0115182342235310.1049/cmu2.12274A multi‐objective method for virtual machines allocation in cloud data centres using an improved grey wolf optimization algorithmMasoud Hashemi0Danial Javaheri1Parisa Sabbagh2Behdad Arandian3Karlo Abnoosian4Zabol University Zabol IranDepartment of Computer Engineering Science and Research Branch Islamic Azad University Tehran IranDISA‐MIS Department University of Salerno Fisciano ItalyDepartment of Electrical Engineering Dolatabad Branch Islamic Azad University Isfahan IranDepartment of Statistics Science and Research Branch Islamic Azad University Tehran IranAbstract Cloud computing is a rapidly evolving computational technology. It is a distributed computational system that offers dynamically scaled computational resources, such as processing power, storage, and applications, delivered as a service through the Internet. Virtual machines (VMs) allocation is known as one of the most significant problems in cloud computing. It aims to find a suitable location for VMs on physical machines (PMs) to attain predefined aims. So, the main purpose is to reduce energy consumption and improve resource utilization. Because the VM allocation issue is NP‐hard, meta‐heuristic and heuristic methods are frequently utilized to address it. This paper presents an energy‐aware VM allocation method using the improved grey wolf optimization (IGWO) algorithm. Our key goals are to decrease both energy consumption and allocation time. The simulation outcomes from the MATLAB simulator approve the excellence of the algorithm compared to previous works.https://doi.org/10.1049/cmu2.12274cloud computingimproved grey wolf optimization algorithmvirtual machines allocationOptimisation techniquesComputer engineeringInternet software |
| spellingShingle | Masoud Hashemi Danial Javaheri Parisa Sabbagh Behdad Arandian Karlo Abnoosian A multi‐objective method for virtual machines allocation in cloud data centres using an improved grey wolf optimization algorithm IET Communications cloud computing improved grey wolf optimization algorithm virtual machines allocation Optimisation techniques Computer engineering Internet software |
| title | A multi‐objective method for virtual machines allocation in cloud data centres using an improved grey wolf optimization algorithm |
| title_full | A multi‐objective method for virtual machines allocation in cloud data centres using an improved grey wolf optimization algorithm |
| title_fullStr | A multi‐objective method for virtual machines allocation in cloud data centres using an improved grey wolf optimization algorithm |
| title_full_unstemmed | A multi‐objective method for virtual machines allocation in cloud data centres using an improved grey wolf optimization algorithm |
| title_short | A multi‐objective method for virtual machines allocation in cloud data centres using an improved grey wolf optimization algorithm |
| title_sort | multi objective method for virtual machines allocation in cloud data centres using an improved grey wolf optimization algorithm |
| topic | cloud computing improved grey wolf optimization algorithm virtual machines allocation Optimisation techniques Computer engineering Internet software |
| url | https://doi.org/10.1049/cmu2.12274 |
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