The top reason for using edge computing is ultra-low latency. But there are other drives, including use cases where processing at the edge alleviates the need to move bulk data generated by bandwidth intensive apps across the wide area network.
Wikibon compared the three-year management and processing costs of a cloud-only solution using AWS IoT services compared with an edge-plus-cloud solution, to support cameras, security sensors, sensors on the wind-turbines and access sensors for all employee physical access points at a remote wind farm.
At a distance of 200 miles between the wind-farm and the cloud, and with an assumed 95 percent reduction in traffic from using the edge computing capabilities, the total cost is reduced from about $81,000 to $29,000 over three years. The cost of edge-plus-cloud computing is about a third the cost of a cloud-only approach, Wikibon estimated.
As always, assumptions are everything. This model assumes a 200-mile distance from the wind-farm to the datacenter, and calculates the transport cost from the edge to the datacenter.
The MPEG video is assumed to be compressed by about 100:1, and the quality assumptions are black/white (only 8 bits/pixel), 1 frame/sec, and 240 x 120 pixels in size.
This is the minimum security quality, and would not provide an effective basis for advanced functions such as facial recognition, Wikibon notes.
The sensor data involves a small amount of data (20 bytes) gathered every three seconds from 100 sensors. The amount of sensor data is about half the low quality MPEG data coming from the 2 video feeds.
In this particular edge computing example, the upside is not so much the latency reduction as the lower spending on wide area network transport, when much data can be processed at the edge, and does not need to be sent to a remote data center.
The cost savings increase as the volume of data volume grows, as when the amount of data to be transmitted is further reduced by about a 100:1 radio.
No comments:
Post a Comment