Energy Efficient Scheduling

Energy-efficient scheduling aims to optimize resource allocation while minimizing energy consumption, a crucial goal across diverse computing systems. Current research focuses on developing algorithms and models, including reinforcement learning and federated learning approaches, to dynamically adapt to fluctuating demands and resource availability, often leveraging predictions from historical data. These advancements are impacting various sectors, from improving the lifespan of satellite constellations and large language model training to optimizing power grids and enhancing the efficiency of wireless networks. The ultimate objective is to create more sustainable and cost-effective systems while maintaining or improving performance.