Nostratheus is a project to use Machine Learning to predict Prometheus metrics
It fetches one or more metric/s for a defined period of time and then traines a model based on time(as X) and it's value in that time (as Y) then saves that in file and an exporter will use that/those file/s to expose new predicted metrics for prometheus.
-
You should config you prometheus host in config file, if there is authentication, you should handle it in code by yourself since for now it just works with unauthenticated hosts.
-
You can define the metric that you are going to fetch in config file and also assign it with a new name which will be exposed to prometheus.
-
Also you can define data period which we use to train the model(by defult it is 1 month)
Tip: If you want to have a lighter trained model to consume less memory, you should choose your period wisely also there is a max_depth=6 which you can change(By increasing this value, accuracy will be more but memory consumption also gets increased)
The book_of_future.py is responsible for training the model and runs just once to train model. You can run it your self or just run pigeon.py which is prometheus exporter and if there is no trained file, runs it and then exposes predicted values to prometheus.
If you had any question, feel free to be in touch with me via maziar.sh110[@]gmail.com