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This paper studies the structure of several real-world traces (includingFacebook, High-Performance Computing, Machine Learning, and simulationgenerated traces) and presents a systematic approach to quantify and comparethe structure of packet traces based on the entropy contained in the tracefile. Insights into the structure of packet traces can lead to improved networkalgorithms that are optimized toward specific traffic patterns. We then presenta methodology to quantify the temporal and non-temporal components of entropycontained in a packet trace, called the trace complexity, using randomizationand compression. We show that trace complexity provides unique insights intothe characteristics of various applications and argue that there is a need fortraffic generation models that preserve the intrinsic structure of empiricallymeasured application traces. We then propose a traffic generator model that isable to produce a synthetic trace that matches the complexity level of itscorresponding real-world trace.

Measuring the Complexity of Packet Traces