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On the Complexity of Traffic Traces and Implications
Name
trace_complexity_sigmetrics_2020.pdf
Description
Accepted version
Size
5.62 MB
Format
Adobe PDF
Checksum (MD5)
bbcdecc00072a13efa40d544ce3d4cb0
Author(s)
Ghobadi, Manya
Date Issued
March 2020
Journal
Proceedings of the ACM on Measurement and Analysis of Computing Systems
Publisher
Association for Computing Machinery (ACM)
Citation
Chen, Avin et al. “On the Complexity of Traffic Traces and Implications.” Proceedings of the ACM on Measurement and Analysis of Computing Systems, 4, 1 (March 2020): Article 20 © 2020 The Author(s)
Version
Author's final manuscript
Abstract
This paper presents a systematic approach to identify and quantify the types of structures featured by packettraces in communication networks. Our approach leverages an information-theoretic methodology, based oniterative randomization and compression of the packet trace, which allows us to systematically remove andmeasure dimensions of structure in the trace. In particular, we introduce the notion oftrace complexitywhichapproximates the entropy rate of a packet trace. Considering several real-world traces, we show that tracecomplexity 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 levelsof its corresponding real-world trace. Using a case study in the context of datacenters, we show that insightsinto the structure of packet traces can lead to improved demand-aware network designs: datacenter topologiesthat are optimized for specific traffic patterns.
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
10.1145/3379486
