# Cloud-Based AI Systems: Leveraging Large Language Models for Intelligent Fault Detection and Autonomous Self-Healing

arXiv: https://arxiv.org/abs/2505.11743 · HTML: https://mlsanity.com/paper/2505.11743/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 34 references that are in the map.
- First posted on arXiv 16 May 2025.

## Builds on (most cited first, 17 of 17)

- [Deception Detection from Linguistic and Physiological Data Streams Using Bimodal Convolutional Neural Networks](https://mlsanity.com/paper/2311.10944/) (2023)
- [Exploring Diverse Methods in Visual Question Answering](https://mlsanity.com/paper/2404.13565/) (2024)
- [ComposerX: Multi-Agent Symbolic Music Composition with LLMs](https://mlsanity.com/paper/2404.18081/) (2024)
- [Visual Large Language Models for Generalized and Specialized Applications](https://mlsanity.com/paper/2501.02765/) (2025)
- [RAG-RLRC-LaySum at BioLaySumm: Integrating Retrieval-Augmented Generation and Readability Control for Layman Summarization of Biomedical Texts](https://mlsanity.com/paper/2405.13179/) (2024)
- [Mitigating the Risk of Health Inequity Exacerbated by Large Language Models](https://mlsanity.com/paper/2410.05180/) (2024)
- [Enhance Image-to-Image Generation with LLaVA-generated Prompts](https://mlsanity.com/paper/2406.01956/) (2024)
- [Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework](https://mlsanity.com/paper/2409.11585/) (2024)
- [MT2ST: Adaptive Multi-Task to Single-Task Learning](https://mlsanity.com/paper/2406.18038/) (2024)
- [HADES: Hardware Accelerated Decoding for Efficient Speculation in Large Language Models](https://mlsanity.com/paper/2412.19925/) (2024)
- [T-GAE: Transferable Graph Autoencoder for Network Alignment](https://mlsanity.com/paper/2310.03272/) (2023)
- [Adaptive Fault Tolerance Mechanisms of Large Language Models in Cloud Computing Environments](https://mlsanity.com/paper/2503.12228/) (2025)
- [Research on Large Language Model Cross-Cloud Privacy Protection and Collaborative Training based on Federated Learning](https://mlsanity.com/paper/2503.12226/) (2025)
- [Scalability Optimization in Cloud-Based AI Inference Services: Strategies for Real-Time Load Balancing and Automated Scaling](https://mlsanity.com/paper/2504.15296/) (2025)
- [Data Augmentation Through Random Style Replacement](https://mlsanity.com/paper/2504.10563/) (2025)
- [MemeBLIP2: A novel lightweight multimodal system to detect harmful memes](https://mlsanity.com/paper/2504.21226/) (2025)
- [Scam Detection for Ethereum Smart Contracts: Leveraging Graph Representation Learning for Secure Blockchain](https://mlsanity.com/paper/2412.12370/) (2024)

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

- [Research on Personalized Financial Product Recommendation by Integrating Large Language Models and Graph Neural Networks](https://mlsanity.com/paper/2506.05873/) (2025)
- [Instruction Tuning and CoT Prompting for Contextual Medical QA with LLMs](https://mlsanity.com/paper/2506.12182/) (2025)
- [Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems](https://mlsanity.com/paper/2506.11421/) (2025)
- [A Blueprint for AI-Driven Software Quality: Integrating LLMs with Established Standards](https://mlsanity.com/paper/2505.13766/) (2025)
- [Anomaly Detection and Early Warning Mechanism for Intelligent Monitoring Systems in Multi-Cloud Environments Based on LLM](https://mlsanity.com/paper/2506.07407/) (2025)
- [QuickMerge++: Fast Token Merging with Autoregressive Prior](https://mlsanity.com/paper/2508.13204/) (2025)
- [E-bike agents: Large Language Model-Driven E-Bike Accident Analysis and Severity Prediction](https://mlsanity.com/paper/2506.04654/) (2025)
- [An Intelligent Fault Self-Healing Mechanism for Cloud AI Systems via Integration of Large Language Models and Deep Reinforcement Learning](https://mlsanity.com/paper/2506.07411/) (2025)
- [Cognitive Load Traces as Symbolic and Visual Accounts of Deep Model Cognition](https://mlsanity.com/paper/2510.15980/) (2025)
- [Safe and Adaptive Cloud Healing: Verifying LLM-Generated Recovery Plans with a Neural-Symbolic World Model](https://mlsanity.com/paper/2607.01595/) (2026)

## Abstract (arXiv)

With the rapid development of cloud computing systems and the increasing complexity of their infrastructure, intelligent mechanisms to detect and mitigate failures in real time are becoming increasingly important. Traditional methods of failure detection are often difficult to cope with the scale and dynamics of modern cloud environments. In this study, we propose a novel AI framework based on Massive Language Model (LLM) for intelligent fault detection and self-healing mechanisms in cloud systems. The model combines existing machine learning fault detection algorithms with LLM's natural language understanding capabilities to process and parse system logs, error reports, and real-time data streams through semantic context. The method adopts a multi-level architecture, combined with supervised learning for fault classification and unsupervised learning for anomaly detection, so that the system can predict potential failures before they occur and automatically trigger the self-healing mechanism. Experimental results show that the proposed model is significantly better than the traditional fault detection system in terms of fault detection accuracy, system downtime reduction and recovery speed.