Regarding measuring CPU load, RAM utilization and other metrics in a slice

There is a REST API that can be used to access the metrics programmatically in addition to the Grafana views.  I will have to add the documentation for that. I’ll post back here once that is done.

Here are the instructions for getting metric data programmatically using curl.

Create a jupyter notebook with the following cells.

# Import MFLib Class
from mflib.mflib import MFLib
slice_name = “<your slice name>”
mf = MFLib(slice_name)

# Get the ht_user & ht_password for the slice’s meas_node.
data = {}
# Set the info you want to get.
data[“get”] = [“ht_user”, “ht_password”]
# Call info using data
info_results = mf.info(“prometheus”, data)
print(info_results)

Alternatively you can just add the second cell above to the existing prometheus_grafana.ipynb notebook.

Create a tunnel thru the bastion host for port 9090. This is similar to the tunnel needed for accessing grafana but using port 9090:localhost:9090 instead of 10010:localhost:443.
The above cell should print out the meas_node_ip as “Found meas node as meas-node at <your meas_node_ip>”

ssh -L 9090:localhost:9090 -F ssh_config -i slice_key ubuntu@<your meas_node ip>

Then make API call using curl or python requests etc…
Here is a simple example using the ht_user and ht_password retrieved above to get the latest up metrics.

curl -k -u <ht_user>:<ht_password> https://localhost:9090/api/v1/query\?query\=up