# Autonomous Resource Management in Microservice Systems via Reinforcement Learning

arXiv: https://arxiv.org/abs/2507.12879 · HTML: https://mlsanity.com/paper/2507.12879/

## Key facts

- Cited by 7 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 3 of its 26 references that are in the map.
- First posted on arXiv 17 Jul 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, 3 of 3)

- [State-Aware IoT Scheduling Using Deep Q-Networks and Edge-Based Coordination](https://mlsanity.com/paper/2504.15577/) (2025)
- [Graph Neural Network-Based Collaborative Perception for Adaptive Scheduling in Distributed Systems](https://mlsanity.com/paper/2505.16248/) (2025)
- [Time-Series Learning for Proactive Fault Prediction in Distributed Systems with Deep Neural Structures](https://mlsanity.com/paper/2505.20705/) (2025)

## Built on by (most cited first, 7 of 7)

- [Multi-Agent Reinforcement Learning for Adaptive Resource Orchestration in Cloud-Native Clusters](https://mlsanity.com/paper/2508.10253/) (2025)
- [Artificial Intelligence-Based Multiscale Temporal Modeling for Anomaly Detection in Cloud Services](https://mlsanity.com/paper/2508.14503/) (2025)
- [Knowledge-Augmented Large Language Model Agents for Explainable Financial Decision-Making](https://mlsanity.com/paper/2512.09440/) (2025)
- [Federated Anomaly Detection for Multi-Tenant Cloud Platforms with Personalized Modeling](https://mlsanity.com/paper/2508.10255/) (2025)
- [Dynamic Anomaly Identification in Accounting Transactions via Multi-Head Self-Attention Networks](https://mlsanity.com/paper/2511.12122/) (2025)
- [Advancing Text Classification with Large Language Models and Neural Attention Mechanisms](https://mlsanity.com/paper/2512.09444/) (2025)
- [Cross-Modal Deep Metric Learning for Time Series Anomaly Detection](https://mlsanity.com/paper/2509.12540/) (2025)

## Abstract (arXiv)

This paper proposes a reinforcement learning-based method for microservice resource scheduling and optimization, aiming to address issues such as uneven resource allocation, high latency, and insufficient throughput in traditional microservice architectures. In microservice systems, as the number of services and the load increase, efficiently scheduling and allocating resources such as computing power, memory, and storage becomes a critical research challenge. To address this, the paper employs an intelligent scheduling algorithm based on reinforcement learning. Through the interaction between the agent and the environment, the resource allocation strategy is continuously optimized. In the experiments, the paper considers different resource conditions and load scenarios, evaluating the proposed method across multiple dimensions, including response time, throughput, resource utilization, and cost efficiency. The experimental results show that the reinforcement learning-based scheduling method significantly improves system response speed and throughput under low load and high concurrency conditions, while also optimizing resource utilization and reducing energy consumption. Under multi-dimensional resource conditions, the proposed method can consider multiple objectives and achieve optimized resource scheduling. Compared to traditional static resource allocation methods, the reinforcement learning model demonstrates stronger adaptability and optimization capability. It can adjust resource allocation strategies in real time, thereby maintaining good system performance in dynamically changing load and resource environments.