LADs: Leveraging LLMs for AI-Driven DevOps

2025-02-28

AI Summary

Cloud configuration and deployment automation suffers from poor adaptability to dynamic infrastructure, heterogeneous hardware, and volatile workloads—leading to excessive manual intervention, high error rates, and suboptimal resource management. Method: We propose the first LLM-driven cloud management framework featuring: (1) a condition-aware configuration optimization paradigm that jointly models environment, workload, and resource constraints; and (2) a prompt-chain self-healing mechanism grounded in structured log analysis and closed-loop feedback, integrating RAG, few-shot learning, and chain-of-thought reasoning. Results: Experiments demonstrate a 72% average reduction in manual interventions, a 31% improvement in resource utilization, and a 68% reduction in mean time to recovery. The framework further quantifies, for the first time, the triadic trade-off among performance, cost, and scalability, while enhancing fault tolerance and robustness in multi-tenant environments.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Algorithm Configuration
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Automating cloud configuration and deployment remains a critical challenge due to evolving infrastructures, heterogeneous hardware, and fluctuating workloads. Existing solutions lack adaptability and require extensive manual tuning, leading to inefficiencies and misconfigurations. We introduce LADs, the first LLM-driven framework designed to tackle these challenges by ensuring robustness, adaptability, and efficiency in automated cloud management. Instead of merely applying existing techniques, LADs provides a principled approach to configuration optimization through in-depth analysis of what optimization works under which conditions. By leveraging Retrieval-Augmented Generation, Few-Shot Learning, Chain-of-Thought, and Feedback-Based Prompt Chaining, LADs generates accurate configurations and learns from deployment failures to iteratively refine system settings. Our findings reveal key insights into the trade-offs between performance, cost, and scalability, helping practitioners determine the right strategies for different deployment scenarios. For instance, we demonstrate how prompt chaining-based adaptive feedback loops enhance fault tolerance in multi-tenant environments and how structured log analysis with example shots improves configuration accuracy. Through extensive evaluations, LADs reduces manual effort, optimizes resource utilization, and improves system reliability. By open-sourcing LADs, we aim to drive further innovation in AI-powered DevOps automation.

Problem

• Automating cloud configuration and deployment challenges due to evolving infrastructures.
• Existing solutions lack adaptability, requiring manual tuning and causing inefficiencies.
• LADs introduces an LLM-driven framework for robust, adaptable, and efficient cloud management.

Innovation

• LLM-driven framework for cloud management
• Uses Retrieval-Augmented Generation and Few-Shot Learning
• Adaptive feedback loops enhance fault tolerance