# Collaborative Multi-Agent Reinforcement Learning Approach for Elastic Cloud Resource Scaling

arXiv: https://arxiv.org/abs/2507.00550 · HTML: https://mlsanity.com/paper/2507.00550/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2025.

## Built on by (most cited first, 10 of 10)

- [Multi-Agent Reinforcement Learning for Adaptive Resource Orchestration in Cloud-Native Clusters](https://mlsanity.com/paper/2508.10253/) (2025)
- [Structure-Learnable Adapter Fine-Tuning for Parameter-Efficient Large Language Models](https://mlsanity.com/paper/2509.03057/) (2025)
- [Dynamic Prompt Fusion for Multi-Task and Cross-Domain Adaptation in LLMs](https://mlsanity.com/paper/2509.18113/) (2025)
- [Contrastive Learning-Based Dependency Modeling for Anomaly Detection in Cloud Services](https://mlsanity.com/paper/2510.13368/) (2025)
- [Knowledge Graph-Infused Fine-Tuning for Structured Reasoning in Large Language Models](https://mlsanity.com/paper/2508.14427/) (2025)
- [Multi-Objective Adaptive Rate Limiting in Microservices Using Deep Reinforcement Learning](https://mlsanity.com/paper/2511.03279/) (2025)
- [Graph Neural Network and Transformer Integration for Unsupervised System Anomaly Discovery](https://mlsanity.com/paper/2508.09401/) (2025)
- [Integrating Feature Attention and Temporal Modeling for Collaborative Financial Risk Assessment](https://mlsanity.com/paper/2508.09399/) (2025)
- [Controllable Abstraction in Summary Generation for Large Language Models via Prompt Engineering](https://mlsanity.com/paper/2510.15436/) (2025)
- [Cross-Modal Deep Metric Learning for Time Series Anomaly Detection](https://mlsanity.com/paper/2509.12540/) (2025)

## Abstract (arXiv)

This paper addresses the challenges of rapid resource variation and highly uncertain task loads in cloud computing environments. It proposes an optimization method for elastic cloud resource scaling based on a multi-agent system. The method deploys multiple autonomous agents to perceive resource states in parallel and make local decisions. While maintaining the distributed nature of the system, it introduces a collaborative value function to achieve global coordination. This improves the responsiveness of resource scheduling and enhances overall system performance. To strengthen system foresight, a lightweight state prediction model is designed. It assists agents in identifying future workload trends and optimizes the selection of scaling actions. For policy training, the method adopts a centralized training and decentralized execution reinforcement learning framework. This enables agents to learn effectively and coordinate strategies under conditions of incomplete information. The paper also constructs typical cloud scenarios, including multi-tenancy and burst traffic, to evaluate the proposed method. The evaluation focuses on resource isolation, service quality assurance, and robustness. Experimental results show that the proposed multi-agent scaling strategy outperforms existing methods in resource utilization, SLA violation control, and scheduling latency. The results demonstrate strong adaptability and intelligent regulation. This provides an efficient and reliable new approach to solving the problem of elastic resource scaling in complex cloud platforms.