Detecting Anomalies in Machine Learning Infrastructure via Hardware Telemetry

📝 Original Info

• Title: Detecting Anomalies in Machine Learning Infrastructure via Hardware Telemetry
• ArXiv ID: 2510.26008
• Date: 2025-10-29
• Authors: ** 해당 논문에 명시된 저자 정보가 제공되지 않았습니다. (필요 시 원문을 확인하거나 저자 목록을 추가해 주세요.) **

📝 Abstract

Modern machine learning (ML) has grown into a tightly coupled, full-stack ecosystem that combines hardware, software, network, and applications. Many users rely on cloud providers for elastic, isolated, and cost-efficient resources. Unfortunately, these platforms as a service use virtualization, which means operators have little insight into the users' workloads. This hinders resource optimizations by the operator, which is essential to ensure cost efficiency and minimize execution time. In this paper, we argue that workload knowledge is unnecessary for system-level optimization. We propose Reveal, which takes a hardware-centric approach, relying only on hardware signals - fully accessible by operators. Using low-level signals collected from the system, Reveal detects anomalies through an unsupervised learning pipeline. The pipeline is developed by analyzing over 30 popular ML models on various hardware platforms, ensuring adaptability to emerging workloads and unknown deployment patterns. Using Reveal, we successfully identified both network and system configuration issues, accelerating the DeepSeek model by 5.97%.

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