Edge AIBench 2.0: Ascalable autonomous vehicle benchmark for IoT--Edge--Cloud systems.

Many emerging IoT--Edge--Cloud computing systems are not yet implemented or are too confidential share the code or even tricky to replicate its execution environment, and hence their benchmarking is challenging. This paper uses autonomous vehicles as a typical scenario to build the first benchmark for Edge--Cloudsystems.We propose a set of distilling rules for replicating autonomous vehicle scenarios to extract critical tasks with intertwined interactions. The essential system-level and component-level characteristics captured while the system complexity is reduced significantly so that users can quickly evaluate and pinpoint the system and component bottlenecks. Also, we implement a scalable architecture through which users assess the systems with different sizes of workloads. We conduct several experiments to measure the performance. After testing two thousand autonomous vehicle task requests, we identify the bottleneck modules in autonomous vehicle scenarios and analyze hotspot functions. The experiment results show that the lane-keeping task is the slowest execution module, with a tail latency of 77.49 ms for the 99th percentile latency. We hope this scenario benchmark will helpful for Autonomous Vehicles and even IoT--edge--Cloud research. Now the open-source code is available from the official website https://www.benchcouncil.org/scenariobench/edgeaibench.html. [ABSTRACT FROM AUTHOR]