Thermal-aware virtual machine placement based on multi-objective optimization

Liu, Bo; Rui, Chen; Lin, Weiwei; Wu, Wentai; Lin, Jianpeng; Li, Keqin

doi:10.1007/s11227-023-05136-z

Cited by 4 publications

(1 citation statement)

References 32 publications

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“…CloudSim simulator is used to validate the performance of the proposed algorithm. The proposed model in [ 21 ] is based on a comprehensive system model that shaped a problem as a constrained multi objective optimization. It includes a novel thermal aware VM placement strategy that considers cost increment, energy consumption, migration cost, and calculation of heat that circulates around the server racks.…”

Section: Literature Reviewmentioning

confidence: 99%

Energy efficient virtual machines placement in cloud datacenters using genetic algorithm and adaptive thresholds

Alourani,

Khalid,

Tahir

et al. 2024

PLoS ONE

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Cloud computing platform provides on-demand IT services to users and advanced the technology. The purpose of virtualization is to improve the utilization of resources and reduce power consumption. Energy consumption is a major issue faced by data centers management. Virtual machine placement is an effective technique used for this purpose. Different algorithms have been proposed for virtual machine placement in cloud environments. These algorithms have considered different parameters. It is obvious that improving one parameter affects other parameters. There is still a need to reduce energy consumption in cloud data centers. Data centers need solutions that reduce energy consumption without affecting other parameters. There is a need to device solutions to effectively utilize cloud resources and reduce energy consumption. In this article, we present an algorithm for Virtual Machines (VMs) placement in cloud computing. The algorithm uses adaptive thresholding to identify over utilized and underutilized hosts to reduce energy consumption and Service Level Agreement (SLA) violations. The algorithm is validated with simulations and comparative results are presented.

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Section: Literature Reviewmentioning

confidence: 99%