Hi Felipe,
I don't think Edgent has such a mechanism for deciding what to run locally and
what to run remotely.
More it's up to the developer to decide what's "too expensive" and what can be
run locally.
For example people-detection in a stream of video images .. the pre-processing
can be done locally without problems, but the heavy lifting of detection should
be done on something a little bigger than a raspberry pi. So in this case I, as
a developer, should setup a system for sending this information to other nodes,
where the further processing is done ... using Kafka or MQTT seems to be a good
option for this.
I think this was even the first presentation I saw Edgent being demonstrated.
Here the images were sent to the Watson IoT Cloud for further processing
(Probably with MQTT).
Hope this helps a little, even if I regret to have to tell you that as far as I
know there's no magic component that helps you with deciding what to do
locally. Also I don't recall Edgent monitoring CPU and Memory usage at all ...
but I could double check that.
Chris
Am 18.11.18, 11:55 schrieb "Felipe Gutierrez" <felipe.o.gutier...@gmail.com>:
Hi,
On the Edgent documentations is mentioned:
"You can send data from an Edgent application to your back-end system when
you need to perform analysis that cannot be performed on the edge device,
such as:
Running a complex analytic algorithm that requires more resources, such as
CPU or memory, than are available on the edge device." (
https://edgent.apache.org/docs/overview)
I understood that the Edgent daemon on the device is constantly monitoring
its properties (CPU and memory) and this daemon decides to not perform the
analytics on the RPi when it has lack of resource but perform on a layer
above. Is it right?
How can I implement a program using Edgent to send this type of metadata to
a layer above? Let's say I want to sent metadata regarding CPU, memory and
storage usage from the RPi to an IoT gateway.
Thanks,
Felipe
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