In Search of a Memory-Efficient Framework for Online Cardinality Estimation

Abstract

Estimating per-flow cardinality from high-speed data streams has many applications such as anomaly detection and resource allocation. Yet despite tracking single flow cardinality with approximation algorithms offered, there remain algorithmical challenges for monitoring multi-flows especially under unbalanced cardinality distribution: existing methods adopt a uniform sketch layout and incur a large memory footprint to achieve high accuracy. Furthermore, they are hard to implement in the compact hardware used for line-rate processing. In this paper, we propose Couper, a memory-efficient measurement framework that can estimate cardinality for multi-flows under unbalanced cardinality distribution. We propose a two-layer structure based on a classic coupon collector's principle, where numerous mice flows are confined to the first layer and only the potential elephant flows are allowed to enter the second layer. Our two-layer structure can better fit the unbalanced cardinality distribution in practice and achieve much higher memory efficiency. We implement Couper in both software and hardware. Extensive evaluation under real-world and synthetic data traces show more than 20× improvements in terms of memory-efficiency compared to state-of-the-art.