NIEF-CS: Nature Inspired Energy Efficient Framework bases on BAT Algorithms for Solving the Cloud Selection Problem
Keywords:
NIEF-CS, BAT-NN, ML techniques, EfficientAbstract
Nowadays, Due to the rapid increase of cloud enabled services, selecting a dependable cloud provider has become extremely complex. A complete review of cloud services from several perspectives needs a precise decision-making process. In view of the vast complexity and limitations of present approaches, which damage the trust of the energy saving cloud selection procedure, further research is necessary to deliver more genuine decision making results. The purpose of this work is for tackling the Cloud Selection problem, a nature-inspired Energy Efficient Framework based on BAT-NN algorithms is used: We provide a new method for forecasting Energy Efficient Cloud Selection (NIEF-CS) in this research, where we have applied our suggested model to compute and predict numerous risk variables. In order to achieve energy saving Cloud selection, and we have compared the model to existing ML techniques like BAT-MM, Genetic-NN, and PSO-NN. We have proposed Nature inspired Energy Efficient Framework bases on BAT-NN algorithms. The UCI ML Repository was selected to collect information on cloud QWS dataset is widely used for study, research and verification of hybrid learning approach with optimal set of concrete services. Results: Our proposed model NIEF-CS achieved the best prediction among ML techniques.
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