Paper Info
Title
Multi-Temporal Analysis and Scaling Relations of 100,000,000,000 Network Packets
Abstract
Our society has never been more dependent on computer networks. Effective utilization of networks requires a detailed understanding of the normal background behaviors of network traffic. Large-scale measurements of networks are computationally challenging. Building on prior work in interactive supercomputing and GraphBLAS hypersparse hierarchical traffic matrices, we have developed an efficient method for computing a wide variety of streaming network quantities on diverse time scales. Applying these methods to 100,000,000,000 anonymized source-destination pairs collected at a network gateway reveals many previously unobserved scaling relationships. These observations provide new insights into normal network background traffic that could be used for anomaly detection, AI feature engineering, and testing theoretical models of streaming networks.
Year
DOI
Venue
2020
10.1109/HPEC43674.2020.9286235
2020 IEEE High Performance Extreme Computing Conference (HPEC)
Keywords
DocType
ISSN
Internet modeling,packet capture,streaming graphs,power-law networks,hypersparse matrices
Conference
2377-6943
ISBN
Citations 
PageRank 
978-1-7281-9220-8
3
0.37
References 
Authors
22
26
Name
Order
Citations
PageRank
Jeremy Kepner160661.58
Chad Meiners230.37
Chansup Byun318019.21
Sarah McGuire430.37
Timothy Davis582.12
William Arcand617517.77
Jonathan Bernays730.37
David Bestor818119.08
William Bergeron9111.81
Vijay Gadepally1044950.53
Raul Harnasch1130.37
Matthew Hubbell1219220.93
Micheal Houle1330.37
Micheal Jones1430.37
Andrew Kirby1530.37
Anna Klein164910.10
Lauren Milechin1710216.45
Julie Mullen1813815.22
Andrew Prout1918218.78
Albert Reuther2033537.32
Antonio Rosa2130.71
Siddharth Samsi2230.71
Doug Stetson2330.37
Adam Tse2430.37
Charles Yee2541.06
Peter Michaleas2641.06