Monitoring network traffic is crucial for most network tasks, such as, identifying and blocking attacks, pinpointing failures and engineering and rerouting heavy traffic to maintain high throughput. One important metric when monitoring the traffic is finding the top-k heavy flows, that is the k heaviest flows in the traffic. Programmable networks allow performing advanced network analysis right in the data plane. In recent years, various solutions have been proposed for efficiently finding the top-k heavy flows within a single switch. However, at times we may need to find the global top-k flows. Existing solutions for global top-k detection use a centralized controller that collects and aggregates the measurements performed in each of the switches. Yet, the process of sending information to the control plane and then having the controller send back the information to the switches can be very lengthy. In order to be able to detect and mitigate short-lived events, solutions that work completely within the data plane are needed. In this paper we present NODE, a network-wide top-k detection algorithm that operates exclusively in the data plane. NODE allows the switches to aggregate information from all other switches in the network, and ensures that eventually all switches hold an identical global top-k table. We show that NODE manages to detect global top-k flows on both synthetic and real traces, with a recall rate of over 95\% while using less than 300KB per switch.

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