In-Network Compression for Accelerating IoT Analytics at Scale

To enable the Internet of Things (IoT) to scale at the level of next generation smart cities and grids, there is a need for cost-effective infrastructure for hosting IoT analytics applications. Offload and acceleration via SmartNICs have been shown to provide benefits to these workloads. However, even with offload, long-term analysis on IoT data still needs to operate on massive number of device updates, often in the form of small messages. Despite offloading, the ingestion of these updates continues to present server bottlenecks. In this paper, we present domain-specific compression and batching engines, that leverage the unique properties of IoT messages to reduce the load on analytics servers and improve their scalability. Using a prototype system based on the InnovaFlex programmable SmartNICs, and several representative IoT benchmarks, we demonstrate that the combination of these techniques achieves up to 14.5× improvement in sustained throughput rates compared to a system without SmartNIC offload, and up to 7× improvement over existing offload approaches.