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
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