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|Abstract:||Many network monitoring tasks identify subsets of traffic that stand out, e.g., top-k flows for a particular statistic. A Protocol Independent Switch Architecture (PISA) switch can identify these "heavy hitter" flows directly in the data plane, by aggregating traffic statistics across packets and comparing against a threshold. However, network operators often want to identify interesting traffic on a network-wide basis. To bridge the gap between line-rate monitoring and network-wide visibility, we present a distributed heavy-hitter detection scheme for networks modeled as one-big switch. We use adaptive thresholds to perform efficient threshold monitoring directly in the data plane. We implement our system using the P4 language, and evaluate it using real-world packet traces. We demonstrate that our solution can accurately detect network-wide heavy hitters with up to 70% savings in communication overhead compared to an existing approach with a provable upper bound.|
|Citation:||Harrison, Rob, Qizhe Cai, Arpit Gupta, and Jennifer Rexford. "Network-Wide Heavy Hitter Detection with Commodity Switches." In Proceedings of the Symposium on SDN Research (2018): pp. 1-7. doi:10.1145/3185467.3185476|
|Pages:||1 - 7|
|Type of Material:||Conference Article|
|Journal/Proceeding Title:||Proceedings of the Symposium on SDN Research|
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