An Elephant in the Room: Using Sampling for Detecting Heavy-Hitters in Programmable Switches

The ability to detect elephant flows in the forwarding device itself, i.e., a switch, facilitates the deployment of new advanced applications such as load-balancing, per-flow QoS management, etc. Sketches and Space Saving summarization techniques are used for elephant flow detection. However, their memory and computing requirements force the cooperation of an external controller device, due to the scarce resources of current programmable switches. To overcome this limitation, we adapt Sketch and Space Saving elephant flow detection techniques to operate with instant notification and sampled traffic. We evaluate the performance of the resulting techniques with three real traffic traces. The use of sampling allows the identification of a large share of the total traffic corresponding to the elephant flows with a low memory footprint and a reduction of the computing requirements in two orders of magnitude compared to unsampled versions. In turn, we observe a slight increase in the number of false positives and the number of flow notifications.The work of Alberto García-Martínez and Marcelo Bagnulo was supported by the TRUE5G Project ('Evolución hacia redes y servicios auto-gestionados para el 5G del futuro') by the Spanish National Research Agency under Grant PID2019-108713RB-C52/AEI/10.13039/501100011033