Analysis of the Cloudlet's Performance Using Machine Learning with Cloud Management System

One of the most widely used methods for hosting and offering services over networks and the Internet is cloud computing, which also includes edge computing. The hardware architecture is built upon the advanced computer technology. Many businesses and academic organizations employ cloud computing. Cloud and edge computing are hampered in terms of security and privacy. Customers frequently have security concerns about the cloud. There are numerous risks that could endanger sensitive user data kept in the cloud, even with privacy regulations in place. In addition to giving a summary of the numerous study findings on cloud privacy issues and tabulating the advantages and disadvantages of recent studies, we classify the current approaches to privacy issues in cloud environments as designs, tactics, and methodology. We also discuss open research topics and offer ideas for new directions to pursue. The principal objective of this research is to elucidate the current gaps in privacy knowledge and offer a more comprehensive grasp of the privacy issues related to cloud computing. The development of the cloud has brought up a number of security concerns that affect both people and corporations. Using machine learning (ML) is one way to secure the cloud. Machine learning methods can be used in a variety of contexts to identify and address cloud security risks. This study has improved IoT device network security detection by using machine learning more frequently.