Lightweight Acquisition and Ranging of Flows in the Data Plane

Author:

Monterubbiano Andrea1ORCID,Langlet Jonatan2ORCID,Walzer Stefan3ORCID,Antichi Gianni4ORCID,Reviriego Pedro5ORCID,Pontarelli Salvatore1ORCID

Affiliation:

1. University of Rome - Sapienza, Rome, Italy

2. Queen Mary University of London, London, United Kingdom

3. Cologne University, Cologne, Germany

4. Politecnico di Milano & Queen Mary University of London, Milan, Italy

5. ETSI de Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain

Abstract

As networks get more complex, the ability to track almost all the flows is becoming of paramount importance. This is because we can then detect transient events impacting only a subset of the traffic. Solutions for flow monitoring exist, but it is getting very difficult to produce accurate estimations for every <flowID,counter> tuple given the memory constraints of commodity programmable switches. Indeed, as networks grow in size, more flows have to be tracked, increasing the number of tuples to be recorded. At the same time, end-host virtualization requires more specific flowIDs, enlarging the memory cost for every single entry. Finally, the available memory resources have to be shared with other important functions as well (e.g., load balancing, forwarding, ACL). To address those issues, we present FlowLiDAR (Flow Lightweight Detection and Ranging), a new solution that is capable of tracking almost all the flows in the network while requiring only a modest amount of data plane memory, which is not dependent on the size of flowIDs. We implemented the scheme in P4, tested it using real traffic from ISPs, and compared it against four state-of-the-art solutions: FlowRadar, NZE, PR-sketch, and Elastic Sketch.

Publisher

Association for Computing Machinery (ACM)

Reference9 articles.

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4. Yuliang Li Rui Miao Changhoon Kim and Minlan Yu. 2016. FlowRadar: A Better NetFlow for Data Centers. In USENIX NSDI.

5. Mariano Scazzariello Tommaso Caiazzi Hamid Ghasemirahni Tom Barbette Dejan Kostic and Marco Chiesa. 2023. A High-Speed Stateful Packet Processing Approach for Tbps Programmable Switches. In Networked Systems Design and Implementation (NSDI). USENIX.

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