Measuring the Complexity of Packet Traces.

Chen Avin, Manya Ghobadi, Chen Griner, Stefan Schmid

Research output: Working paper/PreprintPreprint

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
Original languageEnglish GB
Volumeabs/1905.08339
StatePublished - 2019

Publication series

NameArxiv cs.NI

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