On the Complexity of Traffic Traces and Implications

Chen Avin, Manya Ghobadi, Chen Griner, Stefan Schmid

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

26 Scopus citations

Abstract

This paper presents a systematic approach to identify and quantify the types of structures featured by packet traces in communication networks. Our approach leverages an information-theoretic methodology, based on iterative randomization and compression of the packet trace, which allows us to systematically remove and measure dimensions of structure in the trace. In particular, we introduce the notion of trace complexity which approximates the entropy rate of a packet trace. Considering several real-world traces, we show that trace complexity can provide unique insights into the characteristics of various applications. Based on our approach, we also propose a traffic generator model able to produce a synthetic trace that matches the complexity levels of its corresponding real-world trace. Using a case study in the context of datacenters, we show that insights into the structure of packet traces can lead to improved demand-aware network designs: datacenter topologies that are optimized for specific traffic patterns.

Original languageEnglish
Title of host publicationAbstracts of the 2020 SIGMETRICS
Subtitle of host publicationPerformance Joint International Conference on Measurement and Modeling of Computer Systems
PublisherAssociation for Computing Machinery, Inc
Pages47-48
Number of pages2
ISBN (Electronic)9781450379854
DOIs
StatePublished - 8 Jun 2020
Event2020 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 2020 - Boston, United States
Duration: 8 Jun 202012 Jun 2020

Conference

Conference2020 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 2020
Country/TerritoryUnited States
CityBoston
Period8/06/2012/06/20

Keywords

  • complexity map
  • compress
  • data centers
  • entropy rate
  • self-adjusting networks
  • trace complexity

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Networks and Communications
  • Computational Theory and Mathematics

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