Praxium: Diagnosing Cloud Anomalies with AI-based Telemetry and Dependency Analysis

2026-03-24

AI Summary

This work addresses the challenge of rapidly identifying root causes of anomalies in cloud-native microservice architectures, where frequent continuous deployments hinder timely diagnosis and traditional expert-dependent approaches suffer from poor scalability. The authors propose an end-to-end, AI-driven diagnostic framework that integrates time-series telemetry analysis, causal inference modeling, and software dependency discovery powered by PraxiPaaS. By uniquely correlating causal impact analysis with deployment events, the framework achieves high-precision and interpretable root cause localization. Experimental evaluation demonstrates that the method attains a macro F1-score exceeding 0.97 for anomaly detection across 75 test cases, effectively pinpointing true fault origins even in densely deployed environments.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Reasoning under Uncertainty: Causality

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

As the modern microservice architecture for cloud applications grows in popularity, cloud services are becoming increasingly complex and more vulnerable to misconfiguration and software bugs. Traditional approaches rely on expert input to diagnose and fix microservice anomalies, which lacks scalability in the face of the continuous integration and continuous deployment (CI/CD) paradigm. Microservice rollouts, containing new software installations, have complex interactions with the components of an application. Consequently, this added difficulty in attributing anomalous behavior to any specific installation or rollout results in potentially slower resolution times. To address the gaps in current diagnostic methods, this paper introduces Praxium, a framework for anomaly detection and root cause inference. Praxium aids administrators in evaluating target metric performance in the context of dependency installation information provided by a software discovery tool, PraxiPaaS. Praxium continuously monitors telemetry data to identify anomalies, then conducts root cause analysis via causal impact on recent software installations, in order to provide site reliability engineers (SRE) relevant information about an observed anomaly. In this paper, we demonstrate that Praxium is capable of effective anomaly detection and root cause inference, and we provide an analysis on effective anomaly detection hyperparameter tuning as needed in a practical setting. Across 75 total trials using four synthetic anomalies, anomaly detection consistently performs at >0.97 macro-F1. In addition, we show that causal impact analysis reliably infers the correct root cause of anomalies, even as package installations occur at increasingly shorter intervals.

Problem

• microservice anomalies
• root cause inference
• cloud diagnostics
• software misconfiguration
• CI/CD

Innovation

• anomaly detection
• root cause inference
• causal impact analysis
• microservice telemetry
• dependency-aware diagnosis