{"id":1500,"date":"2023-05-09T15:22:24","date_gmt":"2023-05-09T14:22:24","guid":{"rendered":"https:\/\/thomas-kopton.de\/vblog\/?p=1500"},"modified":"2023-05-09T15:27:23","modified_gmt":"2023-05-09T14:27:23","slug":"energy-consumption-data-in-vmware-aria-operations-for-applications-fka-tanzu-observability-fka-wavefront","status":"publish","type":"post","link":"https:\/\/thomas-kopton.de\/vblog\/?p=1500","title":{"rendered":"Energy Consumption Data in VMware Aria Operations for Applications (FKA Tanzu Observability, FKA Wavefront)"},"content":{"rendered":"\n

My last posts focused on sustainability<\/mark><\/strong> and how VMware Aria Operations<\/mark><\/strong> can help get more insights into energy consumption, infrastructure efficiency and how to improve operations and make the virtual infrastructure more sustainable.<\/p>\n\n\n\n

In this post I will describe how I used an old Raspberry Pi, a DHT22 sensors, few Shelly Plug S smart plugs and VMware Aria Operations for Applications<\/mark><\/strong> (FKA Tanzu Observability, FKA Wavefront) to get environment<\/strong>, power usage<\/strong>, energy consumption<\/strong>, energy costs<\/strong> and carbon emissions<\/strong> insights of my various devices.<\/p>\n\n\n\n

Hardware<\/h5>\n\n\n\n

An old Raspberry Pi Model B Rev 2<\/code> on my desk was waiting for a new usage purpose and I thought it would be a good idea measure the temperature and humidity in my server rack, simply to see if I need any additional cooling. In my case it is rather a proof of concept but in real data centers this information can easily help adjust the cooling system and save energy<\/strong>.<\/p>\n\n\n\n

The DHT22<\/code> sensor and how to attach it to the Pi is very well described here:<\/p>\n\n\n\n

https:\/\/pimylifeup.com\/raspberry-pi-humidity-sensor-dht22<\/a><\/p>\n\n\n\n

I am already getting power usage data from my HPE servers via vSphere and for example VMware Aria Operations, power consumption of all other devices<\/strong> was however a blind spot. A very convenient, providing a well documented API, and not extremely expensive way to get that information (and enable home automation) are the Shelly Smart Devices<\/code>. For this use case I have ordered few of the Smart Plug and Switch devices.<\/p>\n\n\n\n

https:\/\/www.shelly.cloud\/de-ch\/products\/product-overview\/1xplugs\/shelly-plug-s-5-pack<\/a><\/p>\n\n\n\n

Software<\/h5>\n\n\n\n

To process the DHT22 temperature and humidity data I have forked:<\/p>\n\n\n\n

https:\/\/github.com\/adafruit\/Adafruit_Python_DHT<\/a><\/p>\n\n\n\n

And did some modifications to better meet my requirements:<\/p>\n\n\n\n

https:\/\/github.com\/tkopton\/raspberry-pi-dht22-rest-api<\/a><\/p>\n\n\n\n

I know, I need to switch to CircuitPython libraries<\/code>, this is in my backlog;-)<\/p>\n\n\n\n

The small REST server is providing the data I need:<\/p>\n\n\n\n

{\n    \"sensors\": [\n        {\n            \"humidity\": 60.0,\n            \"id\": 1,\n            \"name\": \"Rack01-Bottom\",\n            \"temperature\": 19.5,\n            \"timestamp\": \"2023-05-09T14:05:12.970183\"\n        },\n        {\n            \"humidity\": 53.599998474121094,\n            \"id\": 2,\n            \"name\": \"Rack02-Top\",\n            \"temperature\": 22.299999237060547,\n            \"timestamp\": \"2023-05-09T14:05:12.970183\"\n        }\n    ]\n}<\/code><\/pre>\n\n\n\n
The Shelly devices provide a REST API (and MQTT, but this is something for another blog post) OOTB. The JSON responses include all the needed data for my use cases.<\/p>\n\n\n\n
{\n    \"meters\": [\n        {\n            \"power\": 559.12,\n            \"overpower\": 0.00,\n            \"is_valid\": true,\n            \"timestamp\": 1683640714,\n            \"counters\": [\n                574.193,\n                599.288,\n                597.426\n            ],\n            \"total\": 1728563\n        }\n    ]\n}<\/code><\/pre>\n\n\n\n
To get all these metrics into Aria Operations for Applications<\/strong> I am using a Wavefront Proxy<\/strong> running in my lab and the Telegraf<\/strong> agent configured with the HTTP Plugin.<\/p>\n\n\n\n
The high-level setup is fairly simple:<\/p>\n\n\n\n
\"\"<\/a>
Figure 01: Wavefront-Proxy setup. (Source: https:\/\/docs.wavefront.com\/proxies.html<\/a>)<\/em><\/figcaption><\/figure>\n\n\n\n
The Telegraf HTTP plugin is extremely easy to configure, few lines of config are sufficient to get all the data in. The first example shows the configuration for my Pi+DHT22 sensor and the second one for two Shelly devices (plug and switch):<\/p>\n\n\n\n
[[inputs.http]]\n  urls = [\n    \"http:\/\/192.168.0.151:5000\/api\/v1\/sensors\"\n  ]\n  method = \"GET\"\n\n  timeout = \"10s\"\n  data_format = \"json\"\n  json_string_fields = [\"name\"]<\/code><\/pre>\n\n\n\n
[[inputs.http]]\n  urls = [\n    \"http:\/\/192.168.0.23\/status\/0\"\n  ]\n  method = \"GET\"\n  username = \"admin\"\n  password = \"secret\"\n  timeout = \"10s\"\n  data_format = \"json\"\n  name_override = \"shellyplug_lroom_tv\"\n\n[[inputs.http]]\n  urls = [\n    \"http:\/\/192.168.0.17\/status\/0\"\n  ]\n  method = \"GET\"\n  username = \"admin\"\n  password = \"secret\"\n  timeout = \"10s\"\n  data_format = \"json\"\n  name_override = \"shellyplug_basement_rack\"\n\n[[inputs.http]]\n  urls = [\n    \"http:\/\/192.168.0.24\/rpc\"\n  ]\n  method = \"POST\"\n  body = '''\n  {\"id\":1,\"method\":\"Switch.GetStatus\",\"params\":{\"id\":0}}\n  '''\n  timeout = \"10s\"\n  data_format = \"json\"\n  name_override = \"shellyswitch_kitchen_light_top\"<\/code><\/pre>\n\n\n\n
To calculate the carbon emissions we need to correct kgCO2 per kWh energy factor.<\/p>\n\n\n\n
I am retrieving this real-time value (30-days trial) from:<\/p>\n\n\n\n
https:\/\/app.electricitymaps.com\/map<\/a><\/p>\n\n\n\n
This is the config and after the trial I will switch to another source:<\/p>\n\n\n\n
[[inputs.http]]\n  urls = [\n    \"https:\/\/api-access.electricitymaps.com\/$myID\/carbon-intensity\/latest?zone=DE\"\n  ]\n\n  interval = \"60m\"\n  method = \"GET\"\n  headers = {\"X-BLOBR-KEY\" = \"secret\"}\n  timeout = \"10s\"\n  data_format = \"json\"\n  name_override = \"ElectricityMap_DE\"<\/code><\/pre>\n\n\n\n
Outcome<\/h5>\n\n\n\n
Now it’s time to get insights from the data<\/mark><\/strong>\ud83d\ude42<\/p>\n\n\n\n
As I am still learning the WQL<\/strong> (Wavefront Query Language), these examples might not be perfect but they serve my use cases:<\/p>\n\n\n\n