Virtual Machine Placement Using Energy Efficient Particle Swarm Optimization in Cloud Datacenter
Efficient resource allocation through Virtual machine placement in a cloud datacenter is an ever-growing demand. Different Virtual Machine optimization techniques are constructed for different optimization problems. Particle Swam Optimization (PSO) Algorithm is one of the optimization techniques to...
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Format: | Article |
Language: | English |
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Sciendo
2021-03-01
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Series: | Cybernetics and Information Technologies |
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Online Access: | https://doi.org/10.2478/cait-2021-0005 |
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author | Madhumala R. B. Tiwari Harshvardhan Devaraj Verma C. |
author_facet | Madhumala R. B. Tiwari Harshvardhan Devaraj Verma C. |
author_sort | Madhumala R. B. |
collection | DOAJ |
description | Efficient resource allocation through Virtual machine placement in a cloud datacenter is an ever-growing demand. Different Virtual Machine optimization techniques are constructed for different optimization problems. Particle Swam Optimization (PSO) Algorithm is one of the optimization techniques to solve the multidimensional virtual machine placement problem. In the algorithm being proposed we use the combination of Modified First Fit Decreasing Algorithm (MFFD) with Particle Swarm Optimization Algorithm, used to solve the best Virtual Machine packing in active Physical Machines to reduce energy consumption; we first screen all Physical Machines for possible accommodation in each Physical Machine and then the Modified Particle Swam Optimization (MPSO) Algorithm is used to get the best fit solution.. In our paper, we discuss how to improve the efficiency of Particle Swarm Intelligence by adapting the efficient mechanism being proposed. The obtained result shows that the proposed algorithm provides an optimized solution compared to the existing algorithms. |
first_indexed | 2024-12-18T02:55:54Z |
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id | doaj.art-e66a50d1ad2740788bb0f4bd671e3999 |
institution | Directory Open Access Journal |
issn | 1314-4081 |
language | English |
last_indexed | 2024-12-18T02:55:54Z |
publishDate | 2021-03-01 |
publisher | Sciendo |
record_format | Article |
series | Cybernetics and Information Technologies |
spelling | doaj.art-e66a50d1ad2740788bb0f4bd671e39992022-12-21T21:23:22ZengSciendoCybernetics and Information Technologies1314-40812021-03-01211627210.2478/cait-2021-0005Virtual Machine Placement Using Energy Efficient Particle Swarm Optimization in Cloud DatacenterMadhumala R. B.0Tiwari Harshvardhan1Devaraj Verma C.2Department of Computer Science Engineering, JAIN (Deemed to be University), Bangalore, Karnataka, IndiaCIIRC, Jyothy Institute of Technology, Bangalore, Karnataka, IndiaDepartment of Computer Science Engineering, JAIN (Deemed to be University), Bangalore, Karnataka, IndiaEfficient resource allocation through Virtual machine placement in a cloud datacenter is an ever-growing demand. Different Virtual Machine optimization techniques are constructed for different optimization problems. Particle Swam Optimization (PSO) Algorithm is one of the optimization techniques to solve the multidimensional virtual machine placement problem. In the algorithm being proposed we use the combination of Modified First Fit Decreasing Algorithm (MFFD) with Particle Swarm Optimization Algorithm, used to solve the best Virtual Machine packing in active Physical Machines to reduce energy consumption; we first screen all Physical Machines for possible accommodation in each Physical Machine and then the Modified Particle Swam Optimization (MPSO) Algorithm is used to get the best fit solution.. In our paper, we discuss how to improve the efficiency of Particle Swarm Intelligence by adapting the efficient mechanism being proposed. The obtained result shows that the proposed algorithm provides an optimized solution compared to the existing algorithms.https://doi.org/10.2478/cait-2021-0005cloud computingvirtual machine optimizationparticle swarm optimization (pso)energy efficiencyresource allocationfitness function |
spellingShingle | Madhumala R. B. Tiwari Harshvardhan Devaraj Verma C. Virtual Machine Placement Using Energy Efficient Particle Swarm Optimization in Cloud Datacenter Cybernetics and Information Technologies cloud computing virtual machine optimization particle swarm optimization (pso) energy efficiency resource allocation fitness function |
title | Virtual Machine Placement Using Energy Efficient Particle Swarm Optimization in Cloud Datacenter |
title_full | Virtual Machine Placement Using Energy Efficient Particle Swarm Optimization in Cloud Datacenter |
title_fullStr | Virtual Machine Placement Using Energy Efficient Particle Swarm Optimization in Cloud Datacenter |
title_full_unstemmed | Virtual Machine Placement Using Energy Efficient Particle Swarm Optimization in Cloud Datacenter |
title_short | Virtual Machine Placement Using Energy Efficient Particle Swarm Optimization in Cloud Datacenter |
title_sort | virtual machine placement using energy efficient particle swarm optimization in cloud datacenter |
topic | cloud computing virtual machine optimization particle swarm optimization (pso) energy efficiency resource allocation fitness function |
url | https://doi.org/10.2478/cait-2021-0005 |
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