Workload Prediction

Workload prediction aims to accurately forecast future demands on systems, enabling proactive resource allocation and optimization. Current research focuses on improving prediction accuracy across diverse contexts, employing techniques like hybrid proactive-reactive frameworks, neural networks (including convolutional and transformer architectures), and Bayesian filtering, often tailored to specific data types (e.g., fNIRS, cloud server logs, driving performance data). These advancements are crucial for enhancing efficiency in cloud computing, optimizing resource management in multi-tenant edge platforms, and improving human-machine interaction in safety-critical applications like autonomous driving. The ultimate goal is to create robust and adaptable prediction models that minimize resource waste and maximize system performance.