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- W4386980718 abstract "Allocating virtual machineries in the cloud optimally for workloads is difficult. In the cloud, finding the best way to schedule tasks is an NP-hard issue due to the huge sizes of the tasks involved. The optimal strategy includes allocating work to a data centre full of virtual machines in such a way as to minimise energy consumption, make time, and cost. For that purpose, this paper introduces a hybrid optimisation approach for scheduling jobs. As the butterfly optimisation algorithm (BOA) only takes into account the scent perception criteria, it easily becomes stuck at a local maximum. The suggested butterfly optimisation algorithm (HFBOA) is more in accordance with the real foraging behaviours of butterflies in countryside since it incorporates an additional operator, namely, a colour perception rule, as opposed to the original BOA. In addition, the HFBOA uses logistic mapping to implement an up-to-date technique for controlling parameters, hence improving global optimisation. The simulation findings show that the cloud data canter’s energy consumption, maketime, and cost may all be reduced by improving the scheduling of tasks. In conclusion, the article suggests that using metaheuristic algorithms to agenda tasks on the cloud is a viable option. The simulation answers show that the suggested technique performs very well when used to the solution of difficult, real-world engineering constraints." @default.
- W4386980718 created "2023-09-24" @default.
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- W4386980718 date "2023-01-01" @default.
- W4386980718 modified "2023-10-18" @default.
- W4386980718 title "Task Scheduling Based Optimized Based Algorithm for Minimization of Energy Consumption in Cloud Computing Environment" @default.
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- W4386980718 doi "https://doi.org/10.1007/978-3-031-44084-7_38" @default.
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