Self-Adaptive Microservice Traffic Management: A Reinforcement Learning Approach to Circuit Breaking

Abstract

Microservice applications commonly rely on service-mesh circuit breakers to maintain resilience under dynamic workloads. In platforms such as Istio, circuit breaking is enforced through connection and request-queue limits, making performance highly sensitive to parameter choices that static configurations cannot adapt across traffic phases. This paper proposes an SLO-aware reinforcement learning controller that adjusts Istio circuit-breaker parameters from runtime telemetry. The controller operates in the control plane through standard APIs and requires no changes to application code or data-plane components. A compact state representation and bounded incremental action space support stable, interpretable online adaptation. The controller is trained and evaluated on a real single-node Kubernetes cluster: training uses sustained heavy open-loop overload, while evaluation uses stochastic non-stationary moderate and heavy open-loop workloads. Results show that under heavy overload the controller substantially reduces tail latency relative to unprotected deployments, tuned static circuit breakers, and recent heuristic and adaptive baselines, while matching the strongest baseline on success rate. Under milder load it preserves high success within the latency SLO. These results show that reinforcement learning can provide effective adaptive traffic management for service-mesh environments.