In 2018, data centers alone accounted for 2.7% of the electricity demand in the EU28, according to a recent EU report. The same report expects the energy consumption of data centers to increase by 21% by 2025 as the demand for digital services grows. As communication service providers (CSPs) around the world deploy more 5G services, they will need energy-efficient technologies to help them offset the increases in power consumption. The rollout of 5G networks not only promises to increase demand for new digital services. It is also helping drive the uptake of network functions virtualization (NFV) technology. NFV helps CSPs reduce total cost of ownership and increase their service scalability and agility, including when they expand mobile networks from the network core to the edge. However, despite its benefits, NFV presents significant operational efficiency challenges. The CPUs in VNF servers are kept running at the highest possible frequency. This means that even where there is zero or very low traffic on the network, the servers consume the maximum possible amount of power. Greek operator Cosmote, part of the Deutsche Telekom Group, worked with Intracom Telecom to look at how it could lower the power consumption of VNFs by making it possible for their CPUs to automatically switch to running at lower frequencies during dips in network demand, while still meeting its service level objectives. Cosmote and Intracom Telecom tested Intracom’s NFV Resource Intelligence platform (NFV-RI) on a prototype of its mobile network as part of a trial within the TMF Catalyst, "AIOps Autonomous Service Assurance". NFV-RI uses AI to automatically reduce the energy consumption of the NFV datacenter by dynamically adapting the power of data plane  VNFs to the actual traffic load. The platform works by employing AI in a closed loop to predict what load VNFs will have to cope with at any given moment. The system then automatically adjusts power consumption to match data traffic on the server, while still respecting service level agreements.  The NFV-RI platform’s closed-loop mechanism involves deploying an AI software agent locally on every NFV server, which is able to accurately predict if a short-term traffic overload is about to occur. It automatically calculates exactly when and how to increase CPU frequencies without wasting energy or dropping packets. Equally, the system is set up to detect underload situations so that it can reduce the power available to VNFs that are dealing with low traffic levels.