SLO-Aware Graph Forecasting for Intelligent Autoscaling in API and Microservice Backends
TL;DR
SAGF (SLO-Aware Graph Forecasting), an integrated prediction-and-control framework for proactive autoscaling, encodes six per-service metrics on a spatio-temporal call graph, predicts per-service SLO risk via GCN with temporal gating, and feeds these scores to a constrained RL controller for dependency-aware scaling.
Abstract
Autoscaling microservice backends is challenging due to complex inter-service dependencies, bursty workloads, and cascading latency amplification along call chains. Existing approaches either react to threshold breaches after SLO violations occur, or predict demand per service in isolation. We propose SAGF (SLO-Aware Graph Forecasting), an integrated prediction-and-control framework for proactive autoscaling. SAGF encodes six per-service metrics on a spatio-temporal call graph, predicts per-service SLO risk via GCN with temporal gating, and feeds these scores to a constrained RL controller for dependency-aware scaling. On the Online Boutique benchmark with Kubernetes and Istio, SAGF reduces SLO violations by 41% and excess overprovision by 48% on average compared to the strongest baseline across four workload patterns.