Planter

Abstract

Data classification within the network brings significant benefits in reaction time, servers offload and power efficiency. Still, only very simple models were mapped to the network. In-network classification will not be useful unless we manage to map complex machine learning models to network devices. We present Planter, an algorithm that maps a variety of ensemble models, such as XGBoost and Random Forest, to programmable switches. By overlapping trees within coded tables, Planter manages to map ensemble models to switches with high accuracy and low resource overhead.