Distributed Multi-Task In-Network Classification on Programmable Switches by Ensemble Models

Offloading machine learning models for network classification on high-throughput programmable switches is a promising technology, enabling line-speed in-network classification. Existing solutions are centralized, deploying a complete but heavy model on a single switch with limited hardware resources, causing unsatisfactory accuracy, network-wide resource wastage, and non-generic single-task classification. Therefore, we propose In-Forest-M, a general distributed multi-task in-network classification framework. Firstly, we develop a Lightweight Ensemble Generic Optional Model (LEGO), which can be transformed into base models with full functionality. Each switch only needs to deploy lightweight base models rather than complete ensemble models. The significant reduction in resource consumption allows the deployment of larger models with higher accuracy and more models that support diverse tasks. We employ a fine-grained enhancement mechanism to enhance the classification performance of base models. As traffic traverses different switches, In-Forest-M aggregates the classification results of multiple enhanced base models to improve accuracy further. Secondly, we introduce a two-phase resource-aware model allocation strategy that assigns different task-specific enhanced base models to switches under resource constraints and task requirements. To respond to dynamic traffic changes, we design an optimization-driven reinforcement learning algorithm. Moreover, we propose a lightweight update mechanism for flexible model scaling. Comprehensive experiments reveal that, compared with state-of-the-art in-network classification solutions in three real network topologies, In-Forest-M achieves increased accuracy and reduced switch rules while exhibiting great generality in multi-task classification.