ABSTRACT: The exponential growth of data center operations has raised significant concerns regarding their substantial energy consumption and its environmental impact. Cloud usage is increasing more and more from companies and individuals. The rising use of cloud manufacturing, innovative way of conducting operations to which companies are joining, presents concerns about its environmental impact. In this context, this thesis work aims at developing a methodological framework to boost energy consumption optimization within cloud manufacturing infrastructures. The procedure starts with a comprehensive energy diagnostic, utilizing indirect energy measurement metrics like CPU usage, RAM statistics, network traffic, and more, to pinpoint energy inefficiencies and hotspots within cloud manufacturing systems.