  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.
