# Measuring the Complexity of Packet Traces

## TL;DR

This paper introduces a systematic method to quantify and compare the complexity of packet traces using entropy, providing insights into traffic patterns and enabling the creation of realistic traffic generators.

## Contribution

It presents a novel approach to measure trace complexity through entropy analysis and develops a traffic generator that replicates the complexity of real-world traces.

## Key findings

- Trace complexity varies across different applications.
- The proposed model accurately reproduces the complexity of real traces.
- Entropy-based metrics reveal structural differences in packet traces.

## Abstract

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.