mlsanityData to 14 Oct 2026

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

arXiv 2508.10253 ↗First posted 14 Aug 2025cs.LGcited by 12

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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.

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

11 of 39 references.

References · 11

  1. 2020Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 197
  2. 2021HeterPS: Distributed Deep Learning With Reinforcement Learning Based Scheduling in Heterogeneous Environmentscited by 15
  3. 2021Deep Reinforcement Learning-based Methods for Resource Scheduling in Cloud Computing: A Review and Future Directionscited by 14
  4. 2025Dynamic Operating System Scheduling Using Double DQN: A Reinforcement Learning Approach to Task Optimizationcited by 10
  5. 2025Collaborative Multi-Agent Reinforcement Learning Approach for Elastic Cloud Resource Scalingcited by 10
  6. 2025Clinical NLP with Attention-Based Deep Learning for Multi-Disease Predictioncited by 7
  7. 2025Autonomous Resource Management in Microservice Systems via Reinforcement Learningcited by 7
  8. 2025Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimationcited by 6
  9. 2025Capsule Network-Based Semantic Intent Modeling for Human-Computer Interactioncited by 5
  10. 2025Time-Series Learning for Proactive Fault Prediction in Distributed Systems with Deep Neural Structurescited by 4
  11. 2025Transferable Modeling Strategies for Low-Resource LLM Tasks: A Prompt and Alignment-Based Approachcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 10; 2026: 2.

12 papers.

  1. 2025Trustworthy Summarization via Uncertainty Quantification and Risk Awareness in Large Language Modelscited by 10
  2. 2025Structure-Learnable Adapter Fine-Tuning for Parameter-Efficient Large Language Modelscited by 9
  3. 2025Deep Learning Approach for Clinical Risk Identification Using Transformer Modeling of Heterogeneous EHR Datacited by 5
  4. 2025Machine Learning Approaches to Clinical Risk Prediction: Multi-Scale Temporal Alignment in Electronic Health Recordscited by 2
  5. 2025Deep Learning Approach to Anomaly Detection in Enterprise ETL Processes with Autoencoderscited by 1
  6. 2025Advancing Text Classification with Large Language Models and Neural Attention Mechanismscited by 1
  7. 2025Improving Pattern Recognition of Scheduling Anomalies through Structure-Aware and Semantically-Enhanced Graphscited by 1
  8. 2025Cross-Modal Deep Metric Learning for Time Series Anomaly Detectioncited by 0
  9. 2025Cost-TrustFL: Cost-Aware Hierarchical Federated Learning with Lightweight Reputation Evaluation across Multi-Cloudcited by 0
  10. 2025Shared Representation Learning for High-Dimensional Multi-Task Forecasting under Resource Contention in Cloud-Native Backendscited by 0
  11. 2026MAS-H2: A Hierarchical Multi-Agent System for Holistic Cloud-Native Autoscalingcited by 0
  12. 2026A Self-Calibrating Agentic AI Framework for Autonomous Edge Resource Allocationcited by 0

Similar, not cited

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  1. 2024Reinforcement Learning for Adaptive Resource Scheduling in Complex System Environments
  2. 2025Deep Reinforcement Learning for Job Scheduling and Resource Management in Cloud Computing: An Algorithm-Level Review
  3. 2026Agentic-Kube: A Graph-Enhanced Multi-Agent Reinforcement Learning Framework for Multi-Objective Kubernetes Scheduling
  4. 2026Decentralized Task Scheduling in Distributed Systems: A Deep Reinforcement Learning Approach
  5. 2026Small Model as Master Orchestrator: Learning Unified Agent-Tool Orchestration with Parallel Subtask Decomposition
  6. 2026ARL-Tangram: Unleash the Resource Efficiency in Agentic Reinforcement Learning
  7. 2024Reinforcement Learning-Based Adaptive Load Balancing for Dynamic Cloud Environments
  8. 2025Adaptive AI-based Decentralized Resource Management in the Cloud-Edge Continuum