adaptive resource control

When Does Deep RL Beat Calibrated Baselines? A Benchmark Study on Adaptive Resource Control

A research paper introduces RLScale-Bench, a reproducible benchmark for deep reinforcement learning on adaptive resource control. Testing six DRL algorithms and a calibrated rule-based baseline on Kubernetes autoscaling across six workload patterns, the study finds that the calibrated controller achieves the lowest cost on all workloads, though DRL agents perform better on bursty and flash traffic. Discrete-action DRL algorithms also significantly outperform continuous-action ones in constraint violations.