config.yaml

metrics:
- name: cpu_usage  #metric name in prometheus
  data_store : 
    name : influxdb   
    url: 192.168.1.9
    port: 8086
    user : admin
    pass : admin
    db_name : telegraf
    measurement : cpu
  start_time: '2022-09-14 11:19:00'
  end_time: '2022-09-14 11:20:00'
  query: SELECT mean("usage_idle") *-1 +100 FROM "autogen"."cpu" WHERE ("host" = 'ip-172-31-31-81') AND time >= '2022-09-14 11:19:00' AND time <= '2022-09-14 11:20:00' GROUP BY time(10s) 
  training_interval: 1h #amount of data should be used for training
  forecast_duration: 5m #How data points should be predicted, here it will predict for 5 mins
  forecast_every: 60 #At what interval the app do the predictions 
  forecast_basedon: 60 #Forecast based on past how many data points
  write_back_metric: forecast_cpu_use #Where should it write back the metrics
  models : 
    model_name: prophet
    hyperparameters:
      changepoint_prior_scale : 0.05 #determines the flexibility of the trend changes
      seasonality_prior_scale : 10 #determines the flexibility of the seasonality changes
      holidays_prior_scale : 10 #determines the flexibiity to fit the holidays
      changepoint_range : 0.8 #proportion of the history where the trend changes are applied
      seasonality_mode : additive #whether the mode of seasonality is additive or multiplicative
- name: memory_usage  #metric name in prometheus
  data_store : 
    name : prometheus  
    url: http://192.168.1.9:9090
  start_time: '2022-09-14 11:19:00'
  end_time: '2022-09-14 11:20:00'
  query: 100 - ((node_memory_MemAvailable_bytes{instance="node-exporter:9100"} * 100) / node_memory_MemTotal_bytes{instance="node-exporter:9100"})
  training_interval: 1h #amount of data should be used for training
  forecast_duration: 5m #How data points should be predicted, here it will predict for 5 mins
  forecast_every: 60 #At what interval the app do the predictions 
  forecast_basedon: 60 #Forecast based on past how many data points
  write_back_metric: forecast_mem_usage #Where should it write back the metrics
  models : 
    model_name: prophet
    hyperparameters:
      changepoint_prior_scale : 0.05 #determines the flexibility of the trend changes
      seasonality_prior_scale : 10 #determines the flexibility of the seasonality changes
      holidays_prior_scale : 10 #determines the flexibiity to fit the holidays
      changepoint_range : 0.8 #proportion of the history where the trend changes are applied
      seasonality_mode : additive #whether the mode of seasonality is additive or multiplicative