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Splunk Lantern

Predicting service outages before they occur

 

You can use historical KPI data and machine learning algorithms in Splunk ITSI to predict an outage 20-30 minutes before it happens. This process works best when a service has more than 5 good KPIs and more than 1 week of historical data.

The machine learning algorithm looks for recognizable/predictable KPI behavior, which comes before the service's aggregate health score changes. You can use machine learning within Splunk ITSI to build a model for the service you want to track.

Build a model

  1. Open Splunk ITSI and in the top toolbar click Configuration, then Services.
  2. In the list of services, find the service you want to track. Click the Edit drop-down box to the right of the service name, then click Predictive Analytics.
  3. On this screen you will train and test different machine learning algorithms to determine which one gives the most accurate prediction. Use the instructions on-screen to select a time, algorithm type and algorithm, and click the Train button.

  4. After the model has run, investigate the results,  and click Save.
  5. Test out other algorithms by repeating steps 3 and 4. Note that the recommended model is the model that closely predicts a service’s health score. The recommendation might not be accurate if you change the test period. 

Review the Predictive Analytics score and add it to a glass table

  1. Open Splunk ITSI and in the top toolbar click Dashboards, then Predictive Analytics.
  2. In the list of services, find the service you want to track, and select the recommended algorithm model.

  3. After you have selected a model, Splunk ITSI will calculate the future Service Health Score. Click the Cause Analysis button to review the suggested KPIs.
  4. Click the spyglass icon  to review the SPL. Save it to a notepad to copy it into a glass table later.

  5. In the Splunk ITSI top toolbar, click Glass Tables and select the glass table you'd like to add this score to.
  6. Select the Data Overview icon and click Create Ad hoc search.  
  7. Provide a Data Source Name and paste the copied search string into Search with SPL box. 
  8. Click Apply & Close.
  9. Select the ad hoc search you created to add it to the glass table. This adds a new ad-hoc visualization to the glass table.  
  10. Select the ad-hoc visualization. In the Configuration panel, select your desired threshold setting by clicking the desired Selected Data Field dropdown:
    1. Next30m_avg_hs (number) displays the average prediction.
    2. Next30m_worst_hs (number) displays the worst case prediction.
  11. (Optional) Add dynamic color thresholding to predictive analytics visualizations using the Coloring section in the Configuration panel.  For more information, see configuration options for single value and single value icon visualizations.  
  12. (Optional) Add a drilldown to the Predictive Analytics dashboard using the Drilldown Settings section in the Configuration panel.  
    1. Click + Add Drilldown
    2. For On Click, choose the Link to custom URL option.
    3. In a separate ITSI window, navigate to Dashboards > Predictive Analytics. Select the service and corresponding model to display the heath score prediction. Copy the URL.  
    4. Paste the URL you just copied into the URL field in the Configuration panel.  
    5. Click Apply to save your configuration.  
  13. Select a Visualization Type in the Configuration panel. You cannot use the sparkline or trending value visualization types because the prediction is a static value.  
  14. Click the Save icon.