Multi-Objective Adaptive Rate Limiting in Microservices Using Deep Reinforcement Learning

TL;DR

This paper introduces a deep reinforcement learning-based adaptive rate limiting system for microservices, which dynamically optimizes throughput and latency, outperforming traditional methods in high-load scenarios and real-world deployment.

Contribution

It presents a novel hybrid deep RL architecture combining DQN and A3C for adaptive rate limiting in microservices, addressing limitations of traditional algorithms.

Findings

23.7% throughput improvement

31.4% P99 latency reduction

82% reduction in service degradation incidents

Abstract

As cloud computing and microservice architectures become increasingly prevalent, API rate limiting has emerged as a critical mechanism for ensuring system stability and service quality. Traditional rate limiting algorithms, such as token bucket and sliding window, while widely adopted, struggle to adapt to dynamic traffic patterns and varying system loads. This paper proposes an adaptive rate limiting strategy based on deep reinforcement learning that dynamically balances system throughput and service latency. We design a hybrid architecture combining Deep Q-Network (DQN) and Asynchronous Advantage Actor-Critic (A3C) algorithms, modeling the rate limiting decision process as a Markov Decision Process. The system continuously monitors microservice states and learns optimal rate limiting policies through environmental interaction. Extensive experiments conducted in a Kubernetes cluster environment demonstrate that our approach achieves 23.7% throughput improvement and 31.4% P99 latency reduction compared to traditional fixed-threshold strategies under high-load scenarios. Results from a 90-day production deployment handling 500 million daily requests validate the practical effectiveness of the proposed method, with 82% reduction in service degradation incidents and 68% decrease in manual interventions.