HSS: A Memory-Efficient, Accurate, and Fast Network Measurement Framework in Sliding Windows

Network measurement is indispensable to network management. This paper focuses on three fundamental network measurement tasks: membership query, frequency query, and heavy hitter query. Existing solutions, such as sketches, sliding window algorithms, and the Sliding Sketch framework, struggle to simultaneously achieve memory efficiency, accuracy, real-time operation, and generic application. Accordingly, this paper proposes the Half Sliding Sketch (HSS), an improvement over the state-of-the-art Sliding Sketch framework. The HSS framework is applied to five contemporary sketches for the three aforementioned query tasks. Theoretical analysis reveals that our framework is faster, more memory-efficient and more accurate than the state-of-the-art Sliding Sketch while still being generic. Extensive experimental results reveal that HSS significantly enhances the accuracy for the three query tasks, achieving improvements of 2x to 28.7x, 1.5x to 9x, and 2.4x to 3.6x, respectively. Moreover, in terms of speed, HSS is 1.2x to 1.5x faster than the Sliding Sketch.