Types of Metrics
When analysing time series data you quickly start to identify a common trend in what you are seeing, you will find some metrics you are monitoring will be “stable” that is they will have very repeated patterns and change in a similar way over time, while other metrics will be more chaotic, with a discernible pattern difficult to identify. Take for example two metrics, response time and route number (the number of routes in the routing table), you can see from the charts below that the response time is more chaotic with some pattern but really little stability in the metric, while the route number metric is solid, unwavering.
Comparing Metrics with Themselves
This router meatball is a small office router, with little variation in the routing, however a WAN distribution router would be generally stable, but it would have a little more variability. How could I get an alarm from either of these without configuring some complex static thresholds?
The answer is to baseline the metric as it is and compare your current value against the baseline, this method is very useful for values which are very different on different devices, but you want to know when the metric changes, example are route number, number of users logged in, number of processes running on Linux, response time in general, but especially response time of a service.
The opCharts Dynamic Baseline and Threshold Tool
Overall this is what opTrend does. The sophisticated statistical model it builds is very powerful and helps spots these trends with the baseline tool. We have extended opTrend with some additional functionality so that you can quickly get alerts from metrics which are important to you.
What is really key here is that the baseline tool will detect downward changes as well as upward changes, so if your traffic was reducing outside the baseline you would be alerted.
Establishing a Dynamic Baseline
Firstly I want to calculate my current value, I could use the last value collected, but depending on the stability of the metric this might cause false positives, as NMIS has always supported, using a larger threshold period when calculating the current value can result in more relevant results.
For very stable metrics using a small threshold period is no problem, but for wilder values, a longer period is advised. For response time alerting, using a threshold period of 15 minutes or greater would be a good idea. That means that there is some sustained issue and not just a one off internet blip. However with our route number we might be very happy to use the last value and get warned sooner.
Currently two types of baselines are supported by the baseline tool, the first is what I would call opTrend Lite, which is based on the work of Igor Trubin’s SEDS and SEDS lite, this methods calculates the average value for a small window of time looking back the configured number of weeks, so if my baseline was 1 hour for the last 4 weeks and the time now is 16:40 on 1 June 2020 it would look back and gather the following:
- Week 1: 15:40 to 16:40 on 25 May 2020
- Week 2: 15:40 to 16:40 on 18 May 2020
- Week 3: 15:40 to 16:40 on 11 May 2020
- Week 4: 15:40 to 16:40 on 4 May 2020
With the average of each of these windows of time calculated, I can now build my baseline and compare my current value against that baseline’s value.
Depending on the stability of the metric it might be preferable to use the data from that day. For example if you had a rising and falling value It might be preferable to use just the last 4 to 8 hours of the day for your baseline. Take this interface traffic as an example, the input rate while the output rate is stable with a sudden plateau and is then stable again.
If this was a weekly pattern the multi-day baseline would be a better option, but if this happens more randomly, using the same-day would generate an initial event on the increase, then the event would clear as the ~8Mbps became normal, and then when the value dropped again another alert would be generated.
The delta baseline is only concerned with the amount of change in the baseline, for example from a sample of data from the last 4 hours we would see that the average of a metric is 100, we then take the current value, for example, the spike of 145 below, and we calculate the change as a percentage, which would be a change of 45% resulting in a Critical event level.
The delta baseline configuration then allows for defining the level of the event based on the percentage of change, for the defaults, this would result in a Major, you can see the configuration in the example below, this table is how to visualize the configuration.
- 10 – Warning
- 20 – Minor
- 30 – Major
- 40 – Critical
- 50 – Fatal
If the change is below 10% the level will be normal, between 10% and 20% Minor, and so up to over 50% it will be considered fatal.
In practicality this spike was brief and using the 15 minute threshold period (current is the average of the last 15 minutes) the value for calculating change would be 136 and the resulting change would be 36% so a Major event. The threshold period is dampening the spikes to remove brief changes and allow you to see changes which last longer.
Installing the Baseline Tool
Copy the file to the server and do the following, upgrading will be the same process.
tar xvf Baseline-X.Y.tgz
Working with the Dynamic Baseline and Thresholding Tool
The Dynamic Baseline and Threshold Tool includes various configuration options so that you can tune the algorithm to learn differently depending on the metric being used. The tool comes with several metrics already configured. It is a requirement of the system that the stats modeling is completed for the metric you require to be baseline, this is how the NMIS API extracts statistical information from the performance database.
For more information about the installation and configuration steps required to implement opCharts’ Dynamic Baseline and Thresholding tool, it is all detail in our documentation – here.
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