Fast flow volume estimation

Ran Ben Basat, Gil Einziger, Roy Friedman

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

Abstract

The increasing popularity of jumbo frames means growing variance in the size of packets transmitted in modern networks. Consequently, network monitoring tools must maintain explicit traffic volume statistics rather than settle for packet counting as before. We present constant time algorithms for volume estimations in streams and sliding windows, which are faster than previous work. Our solutions are formally analyzed and are extensively evaluated over multiple real-world packet traces as well as synthetic ones. For streams, we demonstrate a run-Time improvement of up to 2.4X compared to the state of the art. On sliding windows, we exhibit a memory reduction of over 100X on all traces and an asymptotic runtime improvement to a constant. Finally, we apply our approach to hierarchical heavy hitters and achieve an empirical 2.4-7X speedup.

Original languageEnglish
Title of host publicationProceedings of the 19th International Conference on Distributed Computing and Networking, ICDCN 2018
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450363723
DOIs
StatePublished - 4 Jan 2018
Externally publishedYes
Event19th International Conference on Distributed Computing and Networking, ICDCN 2018 - Varanasi, India
Duration: 4 Jan 20187 Jan 2018

Publication series

NameACM International Conference Proceeding Series

Conference

Conference19th International Conference on Distributed Computing and Networking, ICDCN 2018
Country/TerritoryIndia
CityVaranasi
Period4/01/187/01/18

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