With diverse IoT workloads, placing compute and analytics close to where data is collected is becoming increasingly important. We seek to understand what is the performance and the cost implication of running analytics on IoT data at the various available platforms. These workloads can be compute-light, such as outlier detection on sensor data, or compute-intensive, such as object detection from video feeds obtained from drones. In our paper, JANUS, we profile the performance/$ and the compute versus communication cost for a compute-light IoT workload and a compute-intensive IoT workload. In addition, we also look at the pros and cons of some of the proprietary deep-learning object detection packages, such as Amazon Rekognition, Google Vision, and Azure Cognitive Services, to contrast with open-source and tunable solutions, such as Faster R-CNN (FRCNN). We find that AWS IoT Greengrass delivers at least 2X lower latency and 1.25X lower cost compared to all other cloud platforms for the compute-light outlier detection workload. For the compute-intensive streaming video analytics task, an opensource solution to object detection running on cloud VMs saves on dollar costs compared to proprietary solutions provided by Amazon, Microsoft, and Google, but loses out on latency (up to 6X). If it runs on a low-powered edge device, the latency is up to 49X lower.
翻译:随着各种IOT工作量的多样化,将计算和分析结果与数据收集地点相近的位置变得越来越重要。 我们试图了解在各种现有平台上对IOT数据进行分析的性能和成本影响。 这些工作量可以是计算光,例如对传感器数据的异常探测,或从无人驾驶飞机获取的视频反馈中进行物体探测等计算密集型。 在我们的论文JANUS中,我们为计算光光 IOT工作量和计算密集 IOT工作量的计算性能/美元和计算与通信成本之间的低比值。 此外,我们还审视一些专有的深层天体探测包的利弊。 例如亚马逊 Rekognition、Googvision 和 Azure Conitive Services, 与开放源和金枪鱼解决方案(如快速R-CNN ) 对比。 我们发现AWS IOT Greengras提供了至少2 X 低的拉特和1.25 X 较低的成本。 此外,我们还查看了49个专有的深层天文的天文的天文探测工具平台的利, 将运行到高端探测成本。