Applications of virtual machine using multi-objective optimization scheduling algorithm for improving CPU utilization and energy efficiency in cloud computing

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dc.contributor.authorChoudhary, Rajkumar
dc.contributor.authorPerinpanayagam, Suresh
dc.date.accessioned2023-01-06T08:51:34Z
dc.date.available2023-01-06T08:51:34Z
dc.date.issued2022-12-02
dc.description.abstractFinancial costs and energy savings are considered to be more critical on average for computationally intensive workflows, as such workflows which generally require extended execution times, and thus, require efficient energy consumption and entail a high financial cost. Through the effective utilization of scheduled gaps, the total execution time in a workflow can be decreased by placing uncompleted tasks in the gaps through approximate computations. In the current research, a novel approach based on multi-objective optimization is utilized with CloudSim as the underlying simulator in order to evaluate the VM (virtual machine) allocation performance. In this study, we determine the energy consumption, CPU utilization, and number of executed instructions in each scheduling interval for complex VM scheduling solutions to improve the energy efficiency and reduce the execution time. Finally, based on the simulation results and analyses, all of the tested parameters are simulated and evaluated with a proper validation in CloudSim. Based on the results, multi-objective PSO (particle swarm optimization) optimization can achieve better and more efficient effects for different parameters than multi-objective GA (genetic algorithm) optimization can.en_UK
dc.identifier.citationChoudhary R, Perinpanayagam S. (2022) Applications of virtual machine using multi-objective optimization scheduling algorithm for improving CPU utilization and energy efficiency in cloud computing. Energies, Volume 15, Issue 23, December 2022, Article number 9164en_UK
dc.identifier.issn1996-1073
dc.identifier.urihttps://doi.org/10.3390/en15239164
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18883
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectCloudSimen_UK
dc.subjectmulti optimization techniqueen_UK
dc.subjectvirtual machineen_UK
dc.subjecthost machineen_UK
dc.subjectgenetic algorithmen_UK
dc.subjectparticle swarm optimizationen_UK
dc.subjectcloud computingen_UK
dc.titleApplications of virtual machine using multi-objective optimization scheduling algorithm for improving CPU utilization and energy efficiency in cloud computingen_UK
dc.typeArticleen_UK

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