Stability-Aware Proactive Autoscaling Using a Double Deep Q-Network in Cloud Computing Environments

Dynamic workloads and latency-sensitive applications require efficient autoscaling in cloud computing environments. However, most existing approaches rely on reactive mechanisms based on static thresholds, resulting in delayed responses and scaling oscillations under workload uncertainty. To address