| Abstrakt: |
An efficient task scheduling is mandatory in cloud computing for providing virtual resources used to carry out the tasks. An effective allocation of VM with the presence of diverse resource requirements, inaccurate information and uncertainties existing in the system is difficult. In this research, an effective task scheduling is done by using the fuzzy logic (FL) with best-fit-decreasing (BFD) in a cloud computing environment. The developed FL–BFD is optimized using resource usage, power, cost and time. Accordingly, the FL–BFD reallocates virtual machine (VM) in the cloud, based on the user demands. Therefore, the adaptability of FL is leveraged to handle uncertainties and imprecise information, which is helpful for an appropriate allocation of VM using BFD according to user requirements. The developed FL–BFD is analyzed using makespan, execution time, degree of imbalance, energy consumption and service level agreements (SLA) violations. The existing approaches named minimum completion time (MCT), particle swarm optimization (PSO), improved wild horse optimization with levy flight algorithm for task scheduling in cloud computing (IWHOLF-TSC), inverted ant colony optimisation (IACO), fuzzy system and modified particle swarm optimization (FMPSO), and task-scheduling using whale optimization (TSWO) are used for comparison. The makespan of FL–BFD with 1000 tasks is 9.2 ms, which is higher when compared to the IWHOLF-TSC and MCT-PSO. [ABSTRACT FROM AUTHOR] |
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