Note

The code referenced in this document and all published examples with pyedgeconnect are available from the GitHub repository within the examples folder. Each example script contains logic to authenticate to the Orchestrator as documented in the authentication example.

Clone the repository and download the examples with:

$ git clone https://github.com/aruba/pyedgeconnect.git

Collecting Appliance Timeseries Data for Boost Utilization

The focus of this example is retrieving appliance Boost utilization and converting the Bytes Boosted per minute data into estimated average Kbps. This helps compare the values against the configured values which are already in Kbps units.

We can only retrieve minute data from Orchestrator for appliances based on the Statistics Retention for default of 72 hours. We can retrieve more data over time to create a more accurate view of utilization over a longer sample period.

This demo is not meant to replace long-term statistics retention or exporting to a proper data storage solution / database. It assists as a lightweight workflow to get a sense of utilization without the overhead of deploying a database and query logic.

When running collection the code retrieves appliance Boost timeseries data from Orchestrator. The collection can be run for all appliances, or limited to a specific appliance.

When the timeseries data is collected, the data is augmented with additional calculation to estimate Kbps rates.

To convert the Bytes Boosted in a minute to an estimated Kbps rate the raw value is multipled by 8 to convert to bits, divided by 60 to convert to seconds, and then divided by 1000 to convert to Kilobits.

Simplified into a single step, multiply the bytes by 0.0001333

The timeseries data is then stored in a CSV file per-appliance for the collection period for analysis.

Before running the code, make sure that the Python packages in addition to pyedgeconnect are installed as this script leverages the pandas package for processing the collected data.

These are referenced in the requirements.txt file in the boost_timeseries example directory

pip install -r requirements.txt

Data Collection

Note

The timeseries data gathered in this code example is the same data viewed in Orchestrator on the Boost Trends tab: Monitoring->Bandwidth->Boost->Summary

The code will collect timeseries data for one or all appliances from the current time going back 72 hours from Orchestrator.

The code will only collect Boost timeseries data for appliances that are currently in a reachable state to Orchestrator. When polling pyedgeconnect.Orchestrator.get_appliances() the appliance has to have a state with a value of 1 to be considered reachable.

Storage/Analysis Implications

Contiguous minute data will represent 1,440 rows of data for a 24hr period.

Data Calculations

Calculated fields in addition to those natively from Orchestrator per-wan-interface include:

1. Estimated Kbps: The average Kbps of Boosted traffic through the appliance for the minute based on the Bytes Boosted. This is calculated as explained in the introduction by multiplying the bytes value by 0.0001333.

   point.append(point[2] * 0.0001333)
   

Warning

There is no age out of data in existing csv files already collected. Without cleaning up the collection, a large amount of data can be collected over time. This example is meant to inspire what’s possible, not to handle a long-term reporting workflow where data may be stored into a database, aged out on a retention schedule and other production-quality attributes.

Exported Files

It will create CSV files for each appliance collected in the boost_tseries_data sub-directory. The files are named in the format of <hostname>_boost_timeseries.csv.

Example: EC-01_boost_timeseries.csv

Python Script & Orchestrator API calls

Runtime arguments

The python script has multiple runtime arguments defined. A user must specify -c or -r at a minimum to guide collection or reporting of data. The other arguments are optional.

All runtime arguments are as follows:

• -o or --orch

  • Type: String

  • Desc: Specify the Orchestrator IP or FQDN, this can be used to be included in HTML report header as text without requiring connecting to Orchestrator for just reporting on previously collected data in CSV files.

  • Example values: 192.0.2.100 or orchestrator.<company>.com

  • Default value: None

• -a or --appliance

  • Type: String

  • Desc: Specify a single appliance by hostname to either collect data for, or filter for on analysis of existing data files.

  • Default value: None

Running the script to collect data for all appliances:

python boost_timeseries.py

Running the script to collect data for single appliance:

python boost_timeseries.py -a MY-appliance-01

Orchestrator API calls

The three API calls to Orchestrator (outside of authentication) are:

• pyedgeconnect.Orchestrator.get_appliances()

• pyedgeconnect.Orchestrator.get_timeseries_stats_boost_single_appliance()

The get_appliances function gets all appliances from Orchestrator to be able to map hostnames with underlying NePK values, as well as other metadata and state of appliance with Orchestrator.

The get_timeseries_stats_boost_single_appliance will return timeseries data for Boost utilization on an appliance.