Edge Cloud Server Deployment with Machine Learning for 6G Internet of Things

Cloud computing is an important technology that may be used to provide client devices access to a large pool of elastic resources. This can be accomplished via a variety of methods. The greatest issue of these systems, the considerable distance between users and servers, has been solved by the creation of edge cloud computing, in which the cloud servers are positioned at the edge of the network. This kind of cloud computing is a variant of traditional cloud computing. The vast majority of them ignore the decision of where the edge servers should be located, despite the fact that this might have a substantial influence on the effectiveness of the system. In the future, networks like those found in the Internet of Things and 6G Networks will need to be able to support very large numbers of users and servers simultaneously. As a direct consequence of this, we need solutions that are capable of being expanded. In light of these two issues, we propose a server deployment strategy that is based on machine learning and data mining for 6G Internet of Things scenarios. Our method has been shown to be one that is not only effective but also time-saving. In addition, we show that our method is superior to more conventional deployment tactics for Edge Cloud Computing servers in terms of reduced lag time and higher utilisation of available resources. These benefits may be attributed to our methodology. In this article, we do in-depth research of the convergence of 6G and the ML (6G and IOT) in order to examine the new possibilities afforded by 6G technologies in IOT networks and applications. Specifically, we are interested in how 6G technologies might improve the efficiency of IOT networks.