Algorithmic Approach to Virtual Machine Migration in Cloud Computing with Updated SESA Algorithm.

SESA cloud computing migration power consumption virtual machine

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
03 Jul 2023
Historique:
received: 06 03 2023
revised: 25 06 2023
accepted: 25 06 2023
medline: 17 7 2023
pubmed: 14 7 2023
entrez: 14 7 2023
Statut: epublish

Résumé

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.

Identifiants

pubmed: 37447966
pii: s23136117
doi: 10.3390/s23136117
pmc: PMC10347073
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : Princess Nourah bint Abdulrahman University Researchers Supporting Project
ID : PNURSP2023R125

Références

BMC Geriatr. 2019 Aug 19;19(1):223
pubmed: 31426766
J Healthc Eng. 2021 Nov 3;2021:8106467
pubmed: 34777738

Auteurs

Amandeep Kaur (A)

Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab 140401, India.

Saurabh Kumar (S)

Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab 140401, India.

Deepali Gupta (D)

Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab 140401, India.

Yasir Hamid (Y)

Information Security and Engineering Technology, Abu Dhabi Polytechnic, Abu Dhabi 111499, United Arab Emirates.

Monia Hamdi (M)

Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.

Amel Ksibi (A)

Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.

Hela Elmannai (H)

Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.

Shilpa Saini (S)

Department of CSE, Chandigarh University, Mohali 140413, India.

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Classifications MeSH