Entropy-based approaches have been shown to aid various network measurement applications such as load balancing, anomaly detection, and traffic classification. Existing methods that assume out-of-band detection and/or...
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Entropy-based approaches have been shown to aid various network measurement applications such as load balancing, anomaly detection, and traffic classification. Existing methods that assume out-of-band detection and/or use switches merely as accelerators require frequency communication between data and control plane, which increase the burden on the network and are no longer sufficient. To track these challenges, the authors design and implement a switch-native approach for entropy estimation that can run detection function entirely inline on data plane. The authors first present Filter-Sketch, a two-layer sketch that supports frequency estimation with small and static memory allocation by separating elephant flow and mice flow. Then, the authors propose a mechanism based on memory-optimized longest-prefix match (LPM) to attain entropy at a line rate that completely executes in the programmable data plane. The trace-driven evaluation shows that Filter-Sketch achieves higher accuracy than the existing data plane algorithm in entropy estimation, where the relative error decreases by 0.65 on average.
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