Application Performance Management (APM) has many benefits when implemented with the right support structure and sponsorship. It's the key for managing action, going red to green, and trending on performance.
As you strive to achieve new levels of sophistication when creating performance baselines, it is important to consider how you will navigate the oscillating winds of application behavior as the numbers come in from all directions. The behavioral context of the user will highlight key threshold settings to consider as you build a framework for real-time alerting into your APM solution.
This will take an understanding of the application and an analysis of the numbers as you begin looking at user patterns. Metrics play a key role in providing this value through different views across multiple comparisons. Absent from any behavioral learning engines which are now emerging in the APM space, you can begin a high level analysis on your own to come to a common understanding of each business application's performance.
Just as water seeks its own level, an application performance baseline will eventually emerge as you track the real-time performance metrics outlining the high and low watermarks of the application. This will include the occasional anomalous wave that comes crashing through affecting the user experience as the numbers fluctuate.
Depending on transaction volume and performance characteristics there will be a certain level of noise that you will need to squelch to a volume level that can be analyzed. When crunching the numbers and distilling patterns, it will be essential to create three baseline comparisons that you will use like a compass for navigation into what is real and what is an exception.
Real-Time vs. Yesterday
As the real-time performance metrics come in, it is important to watch the application performance at least at the five minute interval as compared to the day before to see if there are any obvious changes in performance.
Real-Time vs. 7 days Ago
Comparing Monday to Sunday may not be relevant if your core business hours are M-F; using the real-time view and comparing it to the same day as the previous week will be more useful - especially if a new release of the application was rolled out over the weekend and you want to know how it compares with the previous week.
Real-Time vs. 10 Day Rolling Average
Using a 10, 15 or 30 day rolling average is helpful in reviewing overall application performance with the business, because everyone can easily understand averages and what they mean when compared against a real-time view.
Capturing real-time performance metrics in five minute intervals is a good place to start. Once you get a better understanding of the application behavior you may increase or decrease the interval as needed. For real-time performance alerting, using the averages will give you a good picture when something is out of pattern, and to report on Service Level Management using percentiles (90%, 95%, etc.), will help create and accurate view for the business. To make it simple to remember, alert on the averages and profile with percentiles.
Conclusion
Operationally there are things you may not want to think about all of the time (e.g. standard deviations, averages, percentiles, etc.), but you have to think about them long enough to create the most accurate picture possible as you begin to distill performance patterns with each business application. This can be accomplished by building meaningful performance baselines that will help feed your Service Level Management processes well into the future.
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For more information on the critical success factors in APM adoption and how this centers around the End-User-Experience (EUE), read The Anatomy of APM and the corresponding blog APM’s DNA – Event to Incident Flow.
Prioritizing Gartner's APM Model