Digital infrastructure consumes huge amounts of electricity directly and indirectly for global electricity consumption. Currently, some of the main consumers of electricity are data centers, high-speed networks, and devices that operate continuously to meet growing computing demands. In the current research, we propose a novel framework that integrates environmental intelligence into digital twins to enable resource-aware process control in digital infrastructure. In the proposed system, we monitored factors including power usage, temperature, and e-waste generation and created an energy profile of routers, switches, and computing nodes across time and usage conditions, generating real-time data to predict variations and impacts. A multi-objective optimization engine was integrated into the system to balance sustainability and performance objectives, with constraints on Service Level Agreement (SLA) adherence and hardware availability. The objective function optimized performance and energy consumption while maintaining network performance. We designed a proof-of-concept framework that acts like a cloud–edge network. The results showed that applying the modelling resulted in a 12.6% reduction in energy consumption and a 9.8% increase in performance under typical load scenarios. The system dynamically rerouted non-critical traffic during peak grid emissions, activated low-power modes during idle periods, and recommended infrastructure upgrades based on thermal hotspot forecasts and energy impact assessments. The proposed framework demonstrates how digital twins can align operational efficiency with sustainability by embedding intelligence into real-time control mechanisms.
Joseph et al. (Fri,) studied this question.