Detecting Anomalies in Machine Learning Infrastructure via Hardware Telemetry

TL;DR

This paper introduces Reveal, a hardware-centric anomaly detection system for ML infrastructure that uses low-level hardware signals and unsupervised learning to identify issues without workload knowledge, improving efficiency.

Contribution

Reveal is a novel system that detects anomalies in ML infrastructure using only hardware signals, enabling system-level optimization without workload details.

Findings

Successfully identified network and system configuration issues.

Accelerated DeepSeek model by 5.97%.

Demonstrated adaptability across various hardware platforms.

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%.