Network monitoring is vital in modern clouds and data center networks that need diverse traffic statistics ranging from flow size distributions to heavy hitters. To cope with increasing network rates and massive traffic volumes, sketch based approximate measurement has been extensively studied to trade the accuracy for memory and computation cost, which unfortunately, is sensitive to hash collisions. This paper presents a clustering-preserving sketch method to be resilient to hash collisions. We provide an equivalence analysis of the sketch in terms of the K-means clustering. Based on the analysis result, we cluster similar network flows to the same bucket array to reduce the estimation variance and use the average to obtain unbiased estimation. Testbed shows that the framework adapts to line rates and provides accurate query results. Real-world trace-driven simulations show that LSS remains stable performance under wide ranges of parameters and dramatically outperforms state-of-the-art sketching structures, with over 10~3 to 10~5 times reduction in relative errors for per-flow queries as the ratio of the number of buckets to the number of network flows reduces from 10% to 0.1%.
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