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.
