Multi-Information Sampling and Mixed Estimation for Multi-Task Spread Measurement With Supercube
Hanwen Zhang, He Huang, Yu-E Sun, Guoju Gao, Zhaojie Wang, Shigang Chen · IEEE Transactions on Networking · 2025
Spread measurement is an essential problem in high-speed networks with broad applications, such as anomaly detection and network telemetry. Network administrators typically need to concurrently monitor the spreads of different types of flows to detect various abnormal behaviors. Although many studies have designed memory-efficient structures, such as sketches, for a specific spread measurement task, they have to deploy multiple sketches to support multiple spread measurement tasks, resulting in significant memory and computational overhead. This paper proposes an efficient multi-task information compression method to simultaneously estimate differently defined flow spreads. We introduce multi-information sampling to capture multi-task spread information from each arriving packet by one pass and store it in off-chip memory, thereby conserving on-chip memory and computational resources. Additionally, we carefully designed a one-access multi-dimensional structure called Supercube to preserve as much spread information as possible while catching up with the line rate, thereby enhancing estimation accuracy. We implement our estimator in hardware using NetFPGA. Experiments based on real Internet traces show that our method reduces the ARE by 83.36% for spread estimation compared to rSkt (SOTA) with 300KB of on-chip memory and increases update throughput by 251.252-fold compared to Supersketch. All source codes are available athttps://github.com/Hanwen808/MIME.