Energy Efficient Cloud Computing Using Artificial Neural Networks - Al-Mudhafar, Haneen Saeed; Ibrahim, Abdullahi Abdu (Research Parks Publishers, 2023)
This effort focuses on building an intelligent, energy-efficient cloud architecture to improve cloud computing infrastructures. The rate at which cloud data gets modernized has increased, leading to more reviews comparing the various modernization methodologies and models. Several wealthy nations, including Turkey, have upgraded to more complicated and energy-efficient cloud infrastructure. We designed a Python application that uses an AI framework to maximize cloud computing's usage of computing resources and clean, renewable energy. This plan outlines concepts for a future neural network-trained digital ecosystem (ANN). The ANN model details energy forecasting tasks within a constrained system. Prominent corporations use AI to design policies to secure their cloud infrastructures and digital assets. Cloud computing systems were modernized by acquiring, normalizing, and transforming their file formats. Most cloud-based infrastructures were updated successfully. This was expected, given digital implementations dominate these systems. We'll investigate the energy consumption of AWS, AZURE, GCP, and Digital Ocean. Since most files were still on paper in 2015, the number of upgrades was modest. By 2020, a large part of cloud computing systems will be converted to digital format, with 98.68% accuracy for all cloud computing systems when trained on 80% of the data and evaluated on 20% of the data. Smart energy-efficient cloud solutions are replacing traditional data centers year by year. Smart energy-efficient cloud systems help preserve cloud computing systems and understand how cloud platforms are modernized and perform in energy prediction.