| Resumo: | The soaring use of machine learning leads to increasing processing demands. As data volume keeps growing,
providing classification services with good machine learning performance, high throughput, low latency, and minimal equipment
overheads becomes a challenge. Offloading machine learning
tasks to network switches can be a scalable solution to this
problem, providing high throughput and low latency. However,
network devices are resource constrained, and lack support for
machine learning functionality. In this paper, we introduce IIsy -
a novel mapping tool of machine learning classification models to
off-the-shelf switches. Using an efficient encoding algorithm, IIsy
enables fitting a range of classification models on switches, coexisting with standard switch functionality. To overcome resource
constraints, IIsy adopts a hybrid approach for ensemble models,
running a small model on a switch and a large model on the
backend. The evaluation shows that IIsy achieves near-optimal
classification results, within minimum resource overheads, and
while reducing the load on the backend by 70% for data-intensive
use cases.
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