Workload Forecasting

Workload forecasting aims to accurately predict future resource demands, primarily in cloud computing and logistics, to optimize resource allocation and improve efficiency. Current research emphasizes improving the accuracy and robustness of predictions, particularly for long-term forecasting, using advanced deep learning models like Bayesian neural networks and transformers, often incorporating techniques like multiscale representation learning and uncertainty quantification. These advancements are crucial for enabling efficient autoscaling in cloud environments, optimizing resource utilization in logistics, and ultimately reducing operational costs and improving service level agreements.