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Neural Computing and Applications

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03.01.2021 | Original Article

A multi-objective Monarch Butterfly Algorithm for virtual machine placement in cloud computing

verfasst von: Mohamed Ghetas

Erschienen in: Neural Computing and Applications | Ausgabe 17/2021

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Abstract

The growing demand for cloud computing adoption presents more challenges for researchers to make cloud computing more efficient and affordable for infrastructure providers and end users. The management of cloud computing involves investing in IT infrastructure in the first phase and investing in energy, maintenance and space costs in the second phase. However, energy costs account for a large portion of cloud management costs, and saving energy consumption can significantly reduce overall cloud management costs. Server consolidation is a strategy to improve data center energy efficiency and resource utilization. Virtual machine (VM) placement is considered one of the main problems with VM consolidation. The VM placement problem aims to reduce the number of active physical machines in data center to reduce data center power consumption and maintenance costs. However, the waste of data center resources has a significant impact on the energy efficiency of the data center, so it should be considered in the VM placement strategy. This paper proposes a new method based on the Monarch Butterfly Optimization algorithm (MBO) called MBO-VM for new virtual machine placement, designed to maximize packaging efficiency and reduce the number of active physical servers. CloudSim toolkit is used to test the efficiency of the proposed MBO-VM approach under real cloud workloads as well as synthetic workloads. Simulation results show that MBO-VM produces significantly better results compared with known VM placement techniques. The proposed MBO-VM can reduce the number of active servers more effectively and maximize the packaging efficiency.

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Titel: A multi-objective Monarch Butterfly Algorithm for virtual machine placement in cloud computing
verfasst von: Mohamed Ghetas
Publikationsdatum: 03.01.2021
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 17/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-020-05559-2

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