Top-k estimation has been a significant research focus in network data stream processing, owing to its broad range of applications. However, the rapid detection of top-k frequent flows in massive network traffic poses considerable challenges, primarily due to the stringent requirements for high-speed packet processing and constraints in resource availability. Invertible sketches, as summary data structures, enable the recovery of top-k flows with limited memory usage and bounded error guarantees. To the best of our knowledge, most existing invertible sketch algorithms suffer from high memory access overhead, which adversely affects their performance in top-k estimation. This article proposes Gentle-Sketch, a high-performance and compact invertible sketch that supports top-k estimation using small and statically allocated memory. Designed around the inherent characteristics of data streams, Gentle-Sketch adopts an adaptive structure that organizes multiple buckets in a specific manner. The entry size per bucket is tailored to match the stream distribution. Gentle-Sketch hashes each flow to multiple buckets and flexibly relocates overflowing flows, improving memory utilization. This mechanism preserves elephant flows and incorporates more mice flows without throughput loss. Extensive experimental results demonstrate that Gentle-Sketch achieves invertibility while maintaining high accuracy and throughput in top-k estimation compared to existing sketch algorithms. In particular, compared to the state-of-the-art Double-Anonymous Sketch, Gentle-Sketch improves estimation precision by over 20% and increases throughput by more than a factor of two.
Xin et al. (Wed,) studied this question.