Improving Dynamic Placement of Virtual Machines in Cloud Data Centers Based on Open-Source Development Model Algorithm

Li, Na; Liu, Xiaoling; Wang, Yu; Mojarad, Musa

doi:10.1007/s10723-023-09651-4

Cited by 5 publications

(1 citation statement)

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“…In [38], the authors presented an open-source development model algorithm to address dynamic virtual machines' placement in the cloud. ODMA(Open Source Development Model Algorithm) is one of the meta-heuristic approaches that is used in this work to consolidate several VMs into a reduced number of hosts.…”

Section: Metaheuristic Approachesmentioning

confidence: 99%

A Harris Hawk Optimisation system for energy and resource efficient virtual machine placement in cloud data centers

H. S.,

T.,

Gupta

et al. 2023

PLoS ONE

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Virtualisation is a major technology in cloud computing for optimising the cloud data centre’s power usage. In the current scenario, most of the services are migrated to the cloud, putting more load on the cloud data centres. As a result, the data center’s size expands resulting in increased energy usage. To address this problem, a resource allocation optimisation method that is both efficient and effective is necessary. The optimal utilisation of cloud infrastructure and optimisation algorithms plays a vital role. The cloud resources rely on the allocation policy of the virtual machine on cloud resources. A virtual machine placement technique, based on the Harris Hawk Optimisation (HHO) model for the cloud data centre is presented in this paper. The proposed HHO model aims to find the best place for virtual machines on suitable hosts with the least load and power consumption. PlanetLab’s real-time workload traces are used for performance evaluation with existing PSO (Particle Swarm Optimisation) and PABFD (Best Fit Decreasing). The performance evaluation of the proposed method is done using power consumption, SLA, CPU utilisation, RAM utilisation, Execution time (ms) and the number of VM migrations. The performance evaluation is done using two simulation scenarios with scaling workload in scenario 1 and increasing resources for the virtual machine to study the performance in underloaded and overloaded conditions. Experimental results show that the proposed HHO algorithm improved execution time(ms) by 4%, had a 27% reduction in power consumption, a 16% reduction in SLA violation and an increase in resource utilisation by 17%. The HHO algorithm is also effective in handling dynamic and uncertain environments, making it suitable for real-world cloud infrastructures.

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Section: Metaheuristic Approachesmentioning

confidence: 99%