Routing Oblivious Measurement Analytics

Ran Ben Basat, Xiaoqi Chen, Gil Einziger, Shir Landau Feibish, Danny Raz, Minlan Yu

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


Network-wide traffic analytics are often needed for various network monitoring tasks. These measurements are often performed by collecting samples at network switches, which are then sent to the controller for aggregation. However, performing such analytics without 'overcounting'' flows or packets that traverse multiple measurement switches is challenging. Therefore, existing solutions often simplify the problem by making assumptions on the routing or measurement switch placement. We introduce AROMA, a measurement infrastructure that generates a uniform sample of packets and flows regardless of the topology, workload and routing. Therefore, AROMA can be deployed in many settings, and can also work in the data plane using programmable PISA switches. The AROMA infrastructure includes controller algorithms that approximate a variety of essential measurement tasks while providing formal accuracy guarantees. Using extensive simulations on real-world network traces, we show that our algorithms are competitively accurate compared to the best existing solutions despite the fact that they make no assumptions on the underlying network or the placement of measurement switches.

Original languageEnglish
Title of host publicationIFIP Networking 2020 Conference and Workshops, Networking 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages9
ISBN (Electronic)9783903176287
StatePublished - Jun 2020
Event2020 IFIP Networking Conference and Workshops, Networking 2020 - Paris, France
Duration: 22 Jun 202025 Jun 2020

Publication series

NameIFIP Networking 2020 Conference and Workshops, Networking 2020


Conference2020 IFIP Networking Conference and Workshops, Networking 2020

Bibliographical note

Funding Information:
Acknowledgements: This work is supported in part by the CNS1834263 grant, the Ben-Gurion University Cyber-Security Research Center, and the Zuckerman Institute.

Publisher Copyright:
© 2020 IFIP.


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