<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</abstract>

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.

Virtual Machine Placement Using Energy Efficient Particle Swarm Optimization in Cloud Datacenter

Department of Computer Science Engineering

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Efficient resource allocation through Virtual machine placement in a cloud datacenter is an ever-growing demand. Different Virtual Machine optimization...

Virtual Machine Placement Using Energy Efficient Particle Swarm Optimization in Cloud Datacenter

Data publikacji: 30 mar 2021

Zakres stron: 62 - 72

Otrzymano: 29 wrz 2020

Przyjęty: 19 lut 2021

DOI: https://doi.org/10.2478/cait-2021-0005

Słowa kluczowe
Cloud computing, Virtual Machine optimization, Particle Swarm Optimization (PSO), Energy Efficiency, Resource Allocation, Fitness Function

© 2021 R. B. Madhumala et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Virtual Machine Placement Using Energy Efficient Particle Swarm Optimization in Cloud Datacenter

Data publikacji: 30 mar 2021

Zakres stron: 62 - 72

Otrzymano: 29 wrz 2020

Przyjęty: 19 lut 2021

DOI: https://doi.org/10.2478/cait-2021-0005

Słowa kluczoweCloud computing, Virtual Machine optimization, Particle Swarm Optimization (PSO), Energy Efficiency, Resource Allocation, Fitness Function

© 2021 R. B. Madhumala et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
Cloud computing, Virtual Machine optimization, Particle Swarm Optimization (PSO), Energy Efficiency, Resource Allocation, Fitness Function