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Algorithmic Approach to Virtual Machine Migration in Cloud Computing with Updated SESA Algorithm
Cloud computing plays an important role in every IT sector. Many tech giants such as Google, Microsoft, and Facebook as deploying their data centres around the world to provide computation and storage services. The customers either submit their job directly or they take the help of the brokers for t...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10347073/ https://www.ncbi.nlm.nih.gov/pubmed/37447966 http://dx.doi.org/10.3390/s23136117 |
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author | Kaur, Amandeep Kumar, Saurabh Gupta, Deepali Hamid, Yasir Hamdi, Monia Ksibi, Amel Elmannai, Hela Saini, Shilpa |
author_facet | Kaur, Amandeep Kumar, Saurabh Gupta, Deepali Hamid, Yasir Hamdi, Monia Ksibi, Amel Elmannai, Hela Saini, Shilpa |
author_sort | Kaur, Amandeep |
collection | PubMed |
description | Cloud computing plays an important role in every IT sector. Many tech giants such as Google, Microsoft, and Facebook as deploying their data centres around the world to provide computation and storage services. The customers either submit their job directly or they take the help of the brokers for the submission of the jobs to the cloud centres. The preliminary aim is to reduce the overall power consumption which was ignored in the early days of cloud development. This was due to the performance expectations from cloud servers as they were supposed to provide all the services through their services layers IaaS, PaaS, and SaaS. As time passed and researchers came up with new terminologies and algorithmic architecture for the reduction of power consumption and sustainability, other algorithmic anarchies were also introduced, such as statistical oriented learning and bioinspired algorithms. In this paper, an indepth focus has been done on multiple approaches for migration among virtual machines and find out various issues among existing approaches. The proposed work utilizes elastic scheduling inspired by the smart elastic scheduling algorithm (SESA) to develop a more energy-efficient VM allocation and migration algorithm. The proposed work uses cosine similarity and bandwidth utilization as additional utilities to improve the current performance in terms of QoS. The proposed work is evaluated for overall power consumption and service level agreement violation (SLA-V) and is compared with related state of art techniques. A proposed algorithm is also presented in order to solve problems found during the survey. |
format | Online Article Text |
id | pubmed-10347073 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103470732023-07-15 Algorithmic Approach to Virtual Machine Migration in Cloud Computing with Updated SESA Algorithm Kaur, Amandeep Kumar, Saurabh Gupta, Deepali Hamid, Yasir Hamdi, Monia Ksibi, Amel Elmannai, Hela Saini, Shilpa Sensors (Basel) Article Cloud computing plays an important role in every IT sector. Many tech giants such as Google, Microsoft, and Facebook as deploying their data centres around the world to provide computation and storage services. The customers either submit their job directly or they take the help of the brokers for the submission of the jobs to the cloud centres. The preliminary aim is to reduce the overall power consumption which was ignored in the early days of cloud development. This was due to the performance expectations from cloud servers as they were supposed to provide all the services through their services layers IaaS, PaaS, and SaaS. As time passed and researchers came up with new terminologies and algorithmic architecture for the reduction of power consumption and sustainability, other algorithmic anarchies were also introduced, such as statistical oriented learning and bioinspired algorithms. In this paper, an indepth focus has been done on multiple approaches for migration among virtual machines and find out various issues among existing approaches. The proposed work utilizes elastic scheduling inspired by the smart elastic scheduling algorithm (SESA) to develop a more energy-efficient VM allocation and migration algorithm. The proposed work uses cosine similarity and bandwidth utilization as additional utilities to improve the current performance in terms of QoS. The proposed work is evaluated for overall power consumption and service level agreement violation (SLA-V) and is compared with related state of art techniques. A proposed algorithm is also presented in order to solve problems found during the survey. MDPI 2023-07-03 /pmc/articles/PMC10347073/ /pubmed/37447966 http://dx.doi.org/10.3390/s23136117 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Kaur, Amandeep Kumar, Saurabh Gupta, Deepali Hamid, Yasir Hamdi, Monia Ksibi, Amel Elmannai, Hela Saini, Shilpa Algorithmic Approach to Virtual Machine Migration in Cloud Computing with Updated SESA Algorithm |
title | Algorithmic Approach to Virtual Machine Migration in Cloud Computing with Updated SESA Algorithm |
title_full | Algorithmic Approach to Virtual Machine Migration in Cloud Computing with Updated SESA Algorithm |
title_fullStr | Algorithmic Approach to Virtual Machine Migration in Cloud Computing with Updated SESA Algorithm |
title_full_unstemmed | Algorithmic Approach to Virtual Machine Migration in Cloud Computing with Updated SESA Algorithm |
title_short | Algorithmic Approach to Virtual Machine Migration in Cloud Computing with Updated SESA Algorithm |
title_sort | algorithmic approach to virtual machine migration in cloud computing with updated sesa algorithm |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10347073/ https://www.ncbi.nlm.nih.gov/pubmed/37447966 http://dx.doi.org/10.3390/s23136117 |
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