Heavy Hitters over Interval Queries
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Abstract (arXiv)
Heavy hitters and frequency measurements are fundamental in many networking applications such as load balancing, QoS, and network security. This paper considers a generalized sliding window model that supports frequency and heavy hitters queries over an interval given at query time. This enables drill-down queries, in which the behavior of the network can be examined in finer and finer granularities. For this model, we asymptotically improve the space bounds of existing work, reduce the update and query time to a constant, and provide deterministic solutions. When evaluated over real Internet packet traces, our fastest algorithm processes packets 90--250 times faster, serves queries at least 730 times quicker and consumes at least 40% less space than the known method.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). First posted on arXiv 28 Apr 2018.
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- 2024Learning-Augmented Frequency Estimation in Sliding Windows17 Sep 2024