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A Comprehensive Survey on Root Cause Analysis in (Micro) Services: Methodologies, Challenges, and Trends
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The complex dependencies and propagative faults inherent in microservices, characterized by a dense network of interconnected services, pose significant challenges in identifying the underlying causes of issues. Prompt identification and resolution of disruptive problems are crucial to ensure rapid recovery and maintain system stability. Numerous methodologies have emerged to address this challenge, primarily focusing on diagnosing failures through symptomatic data. This survey aims to provide a comprehensive, structured review of root cause analysis (RCA) techniques within microservices, exploring methodologies that include metrics, traces, logs, and multi-model data. It delves deeper into the methodologies, challenges, and future trends within microservices architectures. Positioned at the forefront of AI and automation advancements, it offers guidance for future research directions.
Forward citations
Cited by 6 Pith papers
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SDVDiag integrates RLHF and context pruning to raise causal edge detection precision from 14% to 100% in an automated valet parking test, outperforming purely data-driven methods.
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TopoEvo: A Topology-Aware Self-Evolving Multi-Agent Framework for Root Cause Analysis in Microservices
TopoEvo is a topology-aware self-evolving multi-agent framework for root cause analysis in microservices that uses multimodal alignment, vector-quantized symptom tokens, and a hypothesis-evidence-test workflow to sepa...
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Multi-Agent Systems for Root Cause Analysis in Microservices
LATS-RCA applies multi-agent Language Agent Tree Search to automate root cause analysis in microservices, reporting high accuracy on a small open-source Java system but lower accuracy in a complex production environment.
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PropLLM: Propagation-Aware Scene Reconstruction for Network Fault Diagnosis
PropLLM combines hop-by-hop scene reconstruction with LLMs for network fault diagnosis, reporting 3.9% and 4.7% accuracy gains plus 50.8% hallucination reduction on real datasets.
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A Topology-Aware, Memory-Centric Architecture that Separates Root-Cause Derivation from Root-Cause Explanation
OPS CORTEX uses four-tier operational memory to deterministically derive root causes from topology and timing before applying LLMs for explanation in microservice failures.
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Interference-Aware Cross-Application Placement: A Multi-Objective Optimization Approach for Microservice Clusters
Presents a causality-driven multi-objective optimization method for microservice placement that uses necessary and sufficient conditional probabilities of interference estimated from profiling to reduce performance de...
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