  Despite constant improvements in efficiency, today's data centers and
networks consume enormous amounts of energy and this demand is expected to rise
even further. An important research question is whether and how fog computing
can curb this trend. As real-life deployments of fog infrastructure are still
rare, a significant part of research relies on simulations. However, existing
power models usually only target particular components such as compute nodes or
battery-constrained edge devices.
  Combining analytical and discrete-event modeling, we develop a holistic but
granular energy consumption model that can determine the power usage of compute
nodes as well as network traffic and applications over time. Simulations can
incorporate thousands of devices that execute complex application graphs on a
distributed, heterogeneous, and resource-constrained infrastructure. We
evaluated our publicly available prototype LEAF within a smart city traffic
scenario, demonstrating that it enables research on energy-conserving fog
computing architectures and can be used to assess dynamic task placement
strategies and other energy-saving mechanisms.
