Aion: A Memory-Efficient Approach for Long-Term Periodic Flow Detection
Yilin Zhao, Jiawei Huang, Xin Su, Jing Shao, Yijun Li, Jiacheng Xie, Sitan Li · 2025
Sketch-based measurement approaches have recently become a promising solution for detecting periodic flows. However, current sketch approaches struggle to achieve accurate detection of periodic flows due to their short-sighted record of the flow arrival information. Recording the long-term information of flow arrivals could mitigate this issue, while the large memory consumption will hurt the detection accuracy. Consequently, achieving a satisfactory trade-off between memory efficiency and detection accuracy remains a tough challenge. To address this issue, we propose Aion for periodic flow detection. Specifically, Aion uses the Sidon sequence to compress historical flow arrival information in multiple time windows into very small size. Based on the periodicity information from successive time windows, Aion updates the estimated frequencies of periodic flows and promptly evicts non-periodic ones to enable accurate detection of frequent periodic flows. We implement Aion on a P4-based testbed and demonstrate that it achieves superior resource efficiency compared to state-of-the-art approaches. Trace-driven evaluations show that Aion improves F1-Score by up to 9.88×, particularly under small memory conditions.