Edge Cloud Server Deployment with Transmission Power Control through Machine Learning for 6G Internet of Things

Cloud computing is an important technology for bringing a big pool of elastic resources to client devices. Their main drawback has long been the long distance between users and servers, but this has been remedied by Edge Cloud Computing, where the cloud servers are located in the network edge. Edge Cloud Computing is regarded as essential for future networks and consequently, there is plenty of research on how to optimize its operation. However, the vast majority of them ignore the decision of where the edge servers should be deployed, despite how severely this can affect the performance of the system. Furthermore, future networks must also deal with massive amounts of clients and servers, such as the ones characteristic of the Internet of Things and 6G Networks. This demands solutions that are scalable. Given these two points, we propose a Machine Learning-based server deployment policy in 6G Internet of Things environments. Our solution is proven to approach optimality while being feasible. Furthermore, we also prove that our proposal leads to lower latency and higher resource efficiency than conventional Edge Cloud Computing server deployment solutions.