A comparative evaluation of population-based optimization algorithms for workflow scheduling in cloud-fog environments

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dc.contributor.advisorNyirenda, Clement
dc.contributor.authorSubramoney, Dineshan
dc.date.accessioned2023-02-23T07:36:42Z
dc.date.accessioned2024-10-30T14:00:54Z
dc.date.available2023-02-23T07:36:42Z
dc.date.available2024-10-30T14:00:54Z
dc.date.issued2022
dc.description>Magister Scientiae - MScen_US
dc.description.abstractScientific workflows are denoted by interdependent tasks and computations that are aimed at achieving some scientific objectives. The scheduling of these workflows involve the allocation of the tasks to particular computational resources, traditionally on the cloud infrastructure. This process is, however, very challenging. It is associated with high computation and communication costs because scientific workflows are data-intensive and computationally complex. In recent years, there has been overwhelming interest in using population-based optimization algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) for scientific workflow scheduling, predominantly, in the cloud environments.en_US
dc.identifier.urihttps://hdl.handle.net/10566/16977
dc.language.isoenen_US
dc.publisherUniversity of the Western Capeen_US
dc.rights.holderUniversity of the Western Capeen_US
dc.subjectGenetic algorithmsen_US
dc.subjectCloud computingen_US
dc.subjectFog computingen_US
dc.subjectComputer scienceen_US
dc.subjectCybersecurityen_US
dc.titleA comparative evaluation of population-based optimization algorithms for workflow scheduling in cloud-fog environmentsen_US

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