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

arXiv: https://arxiv.org/abs/2511.03279 · HTML: https://mlsanity.com/paper/2511.03279/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 13 of its 41 references that are in the map.
- First posted on arXiv 5 Nov 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 13 of 13)

- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Rainbow: Combining Improvements in Deep Reinforcement Learning](https://mlsanity.com/paper/1710.02298/) (2017)
- [Collaborative Multi-Agent Reinforcement Learning Approach for Elastic Cloud Resource Scaling](https://mlsanity.com/paper/2507.00550/) (2025)
- [Contrastive Learning-Based Dependency Modeling for Anomaly Detection in Cloud Services](https://mlsanity.com/paper/2510.13368/) (2025)
- [Collaborative Evolution of Intelligent Agents in Large-Scale Microservice Systems](https://mlsanity.com/paper/2508.20508/) (2025)
- [Structure-Aware Temporal Modeling for Chronic Disease Progression Prediction](https://mlsanity.com/paper/2508.14942/) (2025)
- [Artificial Intelligence-Based Multiscale Temporal Modeling for Anomaly Detection in Cloud Services](https://mlsanity.com/paper/2508.14503/) (2025)
- [Knowledge Graph-Infused Fine-Tuning for Structured Reasoning in Large Language Models](https://mlsanity.com/paper/2508.14427/) (2025)
- [Graph Neural Network and Transformer Integration for Unsupervised System Anomaly Discovery](https://mlsanity.com/paper/2508.09401/) (2025)
- [Unified Representation Learning for Multi-Intent Diversity and Behavioral Uncertainty in Recommender Systems](https://mlsanity.com/paper/2509.04694/) (2025)
- [Integrating Structure-Aware Attention and Knowledge Graphs in Explainable Recommendation Systems](https://mlsanity.com/paper/2510.10109/) (2025)
- [A Reinforcement Learning-Driven Task Scheduling Algorithm for Multi-Tenant Distributed Systems](https://mlsanity.com/paper/2508.08525/) (2025)

## Built on by (most cited first, 5 of 5)

- [Parameter-Efficient Fine-Tuning with Differential Privacy for Robust Instruction Adaptation in Large Language Models](https://mlsanity.com/paper/2512.06711/) (2025)
- [Deep Q-Learning-Based Intelligent Scheduling for ETL Optimization in Heterogeneous Data Environments](https://mlsanity.com/paper/2512.13060/) (2025)
- [Graph-Structured Deep Learning Framework for Multi-task Contention Identification with High-dimensional Metrics](https://mlsanity.com/paper/2601.20389/) (2026)
- [CaliCausalRank: Calibrated Multi-Objective Ad Ranking with Robust Counterfactual Utility Optimization](https://mlsanity.com/paper/2602.18786/) (2026)
- [Self-Adaptive Multi-Agent LLM-Based Security Pattern Selection for IoT Systems](https://mlsanity.com/paper/2605.00741/) (2026)

## Abstract (arXiv)

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.