HashCuckoo: Predicting Elephant Flows using Meta-Heuristics in Programmable Data Planes
Marcus Vinicius Brito da Silva, Alberto Egon Schaeffer-Filho, Lisandro Zambenedetti Granville · GLOBECOM 2022 - 2022 IEEE Global Communications Conference · 2022
Software-Defined Networking and programmable networks have lead to the development of novel solutions to identify and even predict critical network flows (i.e., flows that can more heavily impact network resources), so they can be properly handled. However, existing approaches found in the state-of-the-art typically incur delays because of the switch-controller communication or depend on thresholds being exceeded to identify flows of interest (e.g., elephant flows). In this paper, we present HashCuckoo, an approach to predict elephant flows that includes: (i) a hash-based mechanism to start the prediction process at line rate in P4 switches, based on the Cuckoo Search meta-heuristic; and (ii) a local prediction mechanism to infer the new flows' traffic behavior, confirming the classification, and handling elephant flows on-line before exceeding traditionally considered thresholds. We evaluate the trade-offs between HashCuckoo and state-of-the-art solutions, and show that HashCuckoo reduces elephant flow identification delay by 57%, from 102 ms to 43 ms, being the first solution to combine meta-heuristic optimization and prediction that can operate at line rate in programmable data planes.