Applying Machine Learning Frameworks to Analyze Large Amounts of Data from IOT Networks in Order to Improve the Efficiency of Cloud Computing Applications: A Review

Abstract

Next-generation cloud computing designs emerged as a response to shortcomings of trend cloud computing concepts. Shallow intelligent algorithms are unable to handle the massive volumes of data produced by the developing cloud computing infrastructures. Researchers have lately begun to pay close attention to “deep learning algorithms” because of their capacity to handle large-scale datasets and use them to address issues in newly developed cloud computing systems. On the other hand, there is currently no thorough literature study available on the application of “deep learning architectures in cloud computing” system development to address challenging issues. We carried out a broad writing review on the uses of “deep learning” architectures in cutting-edge “cloud computing” systems in order to close this gap. There are ramifications for data generation with the new “cloud computing” systems. The massive amounts of data produced by the new paradigms are known as "big data." There's a chance that the data generated won't get the chance to be examined to find out fresh information.