Catch Me If You Can: Using Power Analysis to Identify HPC Activity

TL;DR

This paper demonstrates that analyzing power consumption patterns on HPC systems can effectively identify running programs with high accuracy, offering a non-intrusive monitoring method to detect resource abuse.

Contribution

The study introduces a novel approach using power analysis to identify HPC programs, providing a less invasive alternative to traditional monitoring tools.

Findings

Power consumption patterns can identify programs with 95% precision and recall.

The method remains effective even in noisy operational environments.

Power analysis offers a non-intrusive way to monitor HPC activity.

Abstract

Monitoring users on large computing platforms such as high performance computing (HPC) and cloud computing systems is non-trivial. Utilities such as process viewers provide limited insight into what users are running, due to granularity limitation, and other sources of data, such as system call tracing, can impose significant operational overhead. However, despite technical and procedural measures, instances of users abusing valuable HPC resources for personal gains have been documented in the past \cite{hpcbitmine}, and systems that are open to large numbers of loosely-verified users from around the world are at risk of abuse. In this paper, we show how electrical power consumption data from an HPC platform can be used to identify what programs are executed. The intuition is that during execution, programs exhibit various patterns of CPU and memory activity. These patterns are reflected in the power consumption of the system and can be used to identify programs running. We test our approach on an HPC rack at Lawrence Berkeley National Laboratory using a variety of scientific benchmarks. Among other interesting observations, our results show that by monitoring the power consumption of an HPC rack, it is possible to identify if particular programs are running with precision up to and recall of 95\% even in noisy scenarios.