Resource Scaling

Resource scaling optimizes the allocation of computing resources to meet dynamic demands while minimizing costs and environmental impact. Current research focuses on developing adaptive algorithms, often incorporating machine learning (e.g., hierarchical attention networks, Bayesian methods, and reinforcement learning) to predict resource needs and proactively adjust allocations, considering factors like carbon intensity and service level agreements. These advancements aim to improve efficiency and cost-effectiveness in cloud computing, data centers, and other resource-intensive systems, leading to significant economic and environmental benefits.