Virtual machine placement in cloud data centers using a hybrid multi-verse optimization algorithm

Sasan Gharehpasha

doi:10.1007/s10462-020-09903-9

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Virtual machine placement in cloud data centers using a hybrid multi-verse optimization algorithm

2020-09-27

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Sasan Gharehpasha

Masdari, Mohammad *

Ahmad Jafarian

DOI：10.1007/s10462-020-09903-9

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Abstract

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Cloud computing is a computing paradigm, where a large pool of systems is connected in private or public networks to provide dynamically scalable infrastructure for application, data, and file storage. With the advent of this technology, the cost of power computation, application hosting, content storage, resource wastage, and delivery is reduced significantly. Cloud computing provides the possibility of merely concentrating on business goals instead of expanding hardware resources for users. Challenging work in virtualization technology is the placement of virtual machines under optimal conditions on physical machines in cloud data centers. Optimal placement of virtual machines over physical ones in cloud data centers can lead to the management of resources and prevention of the resources waste. Hereby, a new approach is proposed based on the combination of the hybrid discrete multi-object whale optimization algorithm, multi-verse optimizer with chaotic functions for optimal placement in the cloud data center. The first object of the proposed algorithm is to decrease power consumption, which is consumed in cloud data centers by reducing active physical machines. The second goal is to cut the resource wastage and managing resources using the optimal placement of virtual machines over physical machines in cloud data centers. With this method, the increasing rate of virtual migration to physical machines is prevented. Finally, the results obtained from the proposed algorithm were compared to some algorithms such as first fit, VMPACS, MBFD.

Keywords:

Cloud computing

Virtualization technology

Whale optimization algorithm

Multi-verse optimizer

Chaotic functions

Power consumption

Resource management

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