# Multi-Agent Reinforcement Learning for Adaptive Resource Orchestration in Cloud-Native Clusters

arXiv: https://arxiv.org/abs/2508.10253 · HTML: https://mlsanity.com/paper/2508.10253/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 39 references that are in the map.
- First posted on arXiv 14 Aug 2025.

## Builds on (most cited first, 11 of 11)

- [Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2006.10800/) (2020)
- [HeterPS: Distributed Deep Learning With Reinforcement Learning Based Scheduling in Heterogeneous Environments](https://mlsanity.com/paper/2111.10635/) (2021)
- [Deep Reinforcement Learning-based Methods for Resource Scheduling in Cloud Computing: A Review and Future Directions](https://mlsanity.com/paper/2105.04086/) (2021)
- [Dynamic Operating System Scheduling Using Double DQN: A Reinforcement Learning Approach to Task Optimization](https://mlsanity.com/paper/2503.23659/) (2025)
- [Collaborative Multi-Agent Reinforcement Learning Approach for Elastic Cloud Resource Scaling](https://mlsanity.com/paper/2507.00550/) (2025)
- [Clinical NLP with Attention-Based Deep Learning for Multi-Disease Prediction](https://mlsanity.com/paper/2507.01437/) (2025)
- [Autonomous Resource Management in Microservice Systems via Reinforcement Learning](https://mlsanity.com/paper/2507.12879/) (2025)
- [Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimation](https://mlsanity.com/paper/2505.07674/) (2025)
- [Capsule Network-Based Semantic Intent Modeling for Human-Computer Interaction](https://mlsanity.com/paper/2507.00540/) (2025)
- [Time-Series Learning for Proactive Fault Prediction in Distributed Systems with Deep Neural Structures](https://mlsanity.com/paper/2505.20705/) (2025)
- [Transferable Modeling Strategies for Low-Resource LLM Tasks: A Prompt and Alignment-Based Approach](https://mlsanity.com/paper/2507.00601/) (2025)

## Built on by (most cited first, 12 of 12)

- [Trustworthy Summarization via Uncertainty Quantification and Risk Awareness in Large Language Models](https://mlsanity.com/paper/2510.01231/) (2025)
- [Structure-Learnable Adapter Fine-Tuning for Parameter-Efficient Large Language Models](https://mlsanity.com/paper/2509.03057/) (2025)
- [Deep Learning Approach for Clinical Risk Identification Using Transformer Modeling of Heterogeneous EHR Data](https://mlsanity.com/paper/2511.04158/) (2025)
- [Machine Learning Approaches to Clinical Risk Prediction: Multi-Scale Temporal Alignment in Electronic Health Records](https://mlsanity.com/paper/2511.21561/) (2025)
- [Deep Learning Approach to Anomaly Detection in Enterprise ETL Processes with Autoencoders](https://mlsanity.com/paper/2511.00462/) (2025)
- [Advancing Text Classification with Large Language Models and Neural Attention Mechanisms](https://mlsanity.com/paper/2512.09444/) (2025)
- [Improving Pattern Recognition of Scheduling Anomalies through Structure-Aware and Semantically-Enhanced Graphs](https://mlsanity.com/paper/2512.18673/) (2025)
- [Cross-Modal Deep Metric Learning for Time Series Anomaly Detection](https://mlsanity.com/paper/2509.12540/) (2025)
- [Cost-TrustFL: Cost-Aware Hierarchical Federated Learning with Lightweight Reputation Evaluation across Multi-Cloud](https://mlsanity.com/paper/2512.20218/) (2025)
- [Shared Representation Learning for High-Dimensional Multi-Task Forecasting under Resource Contention in Cloud-Native Backends](https://mlsanity.com/paper/2512.21102/) (2025)
- [MAS-H2: A Hierarchical Multi-Agent System for Holistic Cloud-Native Autoscaling](https://mlsanity.com/paper/2603.07607/) (2026)
- [A Self-Calibrating Agentic AI Framework for Autonomous Edge Resource Allocation](https://mlsanity.com/paper/2607.22400/) (2026)

## Abstract (arXiv)

This paper addresses the challenges of high resource dynamism and scheduling complexity in cloud-native database systems. It proposes an adaptive resource orchestration method based on multi-agent reinforcement learning. The method introduces a heterogeneous role-based agent modeling mechanism. This allows different resource entities, such as compute nodes, storage nodes, and schedulers, to adopt distinct policy representations. These agents are better able to reflect diverse functional responsibilities and local environmental characteristics within the system. A reward-shaping mechanism is designed to integrate local observations with global feedback. This helps mitigate policy learning bias caused by incomplete state observations. By combining real-time local performance signals with global system value estimation, the mechanism improves coordination among agents and enhances policy convergence stability. A unified multi-agent training framework is developed and evaluated on a representative production scheduling dataset. Experimental results show that the proposed method outperforms traditional approaches across multiple key metrics. These include resource utilization, scheduling latency, policy convergence speed, system stability, and fairness. The results demonstrate strong generalization and practical utility. Across various experimental scenarios, the method proves effective in handling orchestration tasks with high concurrency, high-dimensional state spaces, and complex dependency relationships. This confirms its advantages in real-world, large-scale scheduling environments.