  Benchmarking is commonly used in research fields, such as computer
architecture design and machine learning, as a powerful paradigm for rigorously
assessing, comparing, and developing novel technologies. However, the data
centre networking community lacks a standard open-access benchmark. This is
curtailing the community's understanding of existing systems and hindering the
ability with which novel technologies can be developed, compared, and tested.
  We present TrafPy; an open-access framework for generating both realistic and
custom data centre network traffic traces. TrafPy is compatible with any
simulation, emulation, or experimentation environment, and can be used for
standardised benchmarking and for investigating the properties and limitations
of network systems such as schedulers, switches, routers, and resource
managers. To demonstrate the efficacy of TrafPy, we use it to conduct a
thorough investigation into the sensitivity of 4 canonical scheduling
algorithms (shortest remaining processing time, fair share, first fit, and
random) to varying traffic trace characteristics. We show how the fundamental
scheduler performance insights revealed by these tests translate to 4 realistic
data centre network types; University, Private Enterprise, Commercial Cloud,
and Social Media Cloud. We then draw conclusions as to which types of
scheduling policies are most suited to which types of network load conditions
and traffic characteristics, leading to the possibility of application-informed
decision making at the design stage and new dynamically adaptable scheduling
policies. TrafPy is open-sourced via GitHub and all data associated with this
manuscript via RDR.
