Geo Distributed

Geo-distributed computing focuses on optimizing the distribution of computational workloads across geographically dispersed data centers to improve efficiency and sustainability. Current research emphasizes minimizing energy consumption and carbon emissions through techniques like game theory and reinforcement learning, often integrated with federated learning for improved privacy and resource allocation. This work is crucial for managing the increasing demands of AI and machine learning, particularly in resource-intensive applications like AI model training and inference, leading to more cost-effective and environmentally responsible large-scale computing.