SplitScaling: Adaptive Scaling for Disaggregated LLM Serving Against Traffic Bursts via DRL
This work proposes a Deep Reinforcement Learning-based auto-scaling framework tailored for the PD architecture that enables the agent to capture non-linear load dynamics, thereby achieving decoupled and precise scaling for prefill and decode pools.