In the context of High Performance Computing, scheduling
is a necessary tool to ensure that there exists acceptable
quality of service for the many users of the processing power
available. The scheduling process can vary from a simple First
Comes First Served model to a wide variety of more complex
implementations that tend to satisfy specific requirements
from each group of users. Slurm is an open source, faulttolerant,
and highly scalable cluster management system for
large and small Linux clusters [1]. MareNostrum 4, a High
Performance Computer, implements it to manage the execution
of jobs send to it by a variety of users [2]. Previous work
has been done from an algorithmic approach that attempts
at directly reduce queuing times among other costs [3][4].
We consider that there is utility at looking at the problem
also from a Game Theoretic perspective to define clearly the
mechanics involved in the system, and also those that define
the influx of tasks that the scheduler manages. We model the
Slurm scheduling mechanism using Game Theoretic concepts,
tools, and reasonable simplifications in an attempt to formally
characterize and study it. We identify variables that play a
significant role in the scheduling process and also experiment
with changes in the model that could make users behave
in a way that would improve overall quality of service. We
recognize that the complexity of the models might derive in
difficulty to theoretically analyze them, so we make use of
usage data derived from real usage from BSC-CNS users to
measure performance. The real usage data is extracted from
Autosubmit [5], a workflow manager developed at the Earth
Science Department at BSC-CNS. This is a convenient choice,
given that we also attempt to measure the influence of an
external agent (e.g. a workflow manager) could have in the
overall quality of service if it imposes restrictions, and the
nature of these restrictions
