  This paper studies the structure of several real-world traces (including
Facebook, High-Performance Computing, Machine Learning, and simulation
generated traces) and presents a systematic approach to quantify and compare
the structure of packet traces based on the entropy contained in the trace
file. Insights into the structure of packet traces can lead to improved network
algorithms that are optimized toward specific traffic patterns. We then present
a methodology to quantify the temporal and non-temporal components of entropy
contained in a packet trace, called the trace complexity, using randomization
and compression. We show that trace complexity provides unique insights into
the characteristics of various applications and argue that there is a need for
traffic generation models that preserve the intrinsic structure of empirically
measured application traces. We then propose a traffic generator model that is
able to produce a synthetic trace that matches the complexity level of its
corresponding real-world trace.
