Confluence: improving network monitoring accuracy on multi-pipeline data plane

Abstract A sketch-based method is promising for traffic monitoring in data center networks. Existing data plane programming model (e.g. P4) assumes target switch as one single pipeline, while state-of-the-art programmable switches actually contain multiple independent pipelines. The status quo approach for deploying a sketch-based measurement application on a multi-pipeline switch is to deploy a sketch instance in each pipeline individually. However, under multi-path routing, such a naive approach leads to poor accuracy. To overcome this problem, in this paper, we present Confluence, a sketch-based network measurement system for multi-pipeline switches. For monitoring network flows that have packets arrived in bursts and spread over multiple pipelines, Confluence introduces novel data structures to collect short-term traffic statistics in ingress pipelines, and converge the measurement data to egress pipelines. Confluence is carefully designed under the switch hardware constraints, and in particular, to resolve the circular dependency in querying and updating a flow’s measurement data from sketch buckets, we propose a novel algorithm and theoretically prove its effectiveness. Both theoretical analysis and experiments driven by real-world traffic traces show that Confluence delivers higher measurement accuracies than existing solutions, especially in the critical task of detecting heavy hitters. Assessment on hardware switch suggests that Confluence is practical for real-world deployment.