[2603.23890] Praxium: Diagnosing Cloud Anomalies with AI-based Telemetry and Dependency Analysis

Computer Science > Software Engineering arXiv:2603.23890 (cs) [Submitted on 25 Mar 2026] Title:Praxium: Diagnosing Cloud Anomalies with AI-based Telemetry and Dependency Analysis Authors:Rohan Kumar, Jason Li, Zongshun Zhang, Syed Mohammad Qasim, Gianluca Stringhini, Ayse Kivilcim Coskun View a PDF of the paper titled Praxium: Diagnosing Cloud Anomalies with AI-based Telemetry and Dependency Analysis, by Rohan Kumar and 5 other authors View PDF HTML (experimental) 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 ...