International Journal of Computer Networks and Applications (IJCNA)

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International Journal of Computer Networks and Applications (IJCNA)

International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

SOAVMP: Multi-Objective Virtual Machine Placement in Cloud Computing Based on the Seagull Optimization Algorithm

Author NameAuthor Details

Aristide Ndayikengurukiye, Rim Doukha, Eric Niyukuri, Eric Muheto,Abderrahmane Ez-zahout, Fouzia Omary

Aristide Ndayikengurukiye[1]

Rim Doukha[2]

Eric Niyukuri[3]

Eric Muheto[4]

Abderrahmane Ez-zahout[5]

Fouzia Omary[6]

[1]Intelligent Processing and Security of Systems Team, Faculty of Sciences, Mohammed V University, Rabat, Morocco.

[2]Intelligent Processing and Security of Systems Team, Faculty of Sciences, Mohammed V University, Rabat, Morocco.

[3]Faculty of Engineering Sciences, University of Burundi, Bujumbura, Burundi.

[4]Morgan Stanley, Montréal, Canada.

[5]Intelligent Processing and Security of Systems Team, Faculty of Sciences, Mohammed V University, Rabat, Morocco.

[6]Intelligent Processing and Security of Systems Team, Faculty of Sciences, Mohammed V University, Rabat, Morocco.

Cite this Article

DOI: 10.22247/ijcna/2024/24

Pages : 375-389

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Abstract

Virtual machine placement (VMP) involves selecting the most appropriate physical machine (PM) to run a virtual machine (VM) in cloud data centers (CDCs). Unfortunately, current VMP methods only consider limited resources, resulting in load imbalance and unnecessary activation of certain PMs in the data center (DC). This paper proposes a new approach called Multi-Objective Seagull Optimization Algorithm Virtual Machine (MOSOAVMP) to address these issue s and enhance resource management in CDCs. The aim is to optimize resource utilization, minimize energy consumption, reduce SLA violations, and improve overall DC efficiency. The aim is to achieve an optimal deployment that will meet these different objectives while minimizing the costs associated with operating the CDCs. The results show the proposed MOSOAVMP's efficiency compared with existing algorithms for the different measurements considered. These experimental results show that MOSOAVMP reduces resource wastages, and energy consumption by 5.44%, improves average CPU usage by 14.84%, memory usage by 11.54%, average storage usage by 5.37%, and average bandwidth usage by 6.88%.

Index Terms

Cloud Computing

Seagull Optimization Algorithm

Metaheuristics Algorithm

SLA

Virtual Machine Placement

Data Center

Power Consumption

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