Personal web server - CHAPTER 12 TRENDING, FORECASTING, AND CAPACITY PLANNING
CHAPTER 12 TRENDING, FORECASTING, AND CAPACITY PLANNING Average, or mean, response time Maximum response time Total response time Standard deviation The mean response time is important to quantify, because it represents your end-user experience. But when things go wrong in the application, the response time deviation is even more important to identify, because it reveals how wrong things have gone and helps to diagnose why. The maximum response time also lets you know if you have violated any of your SLAs. The total response time for a particular time segment reveals how important that particular request is to the business it indicates which requests have the greatest impact on your environment. While the first three metrics are obviously important, most people tend to ignore the last one: the standard deviation of service request response time. The standard deviation tells you how much distribution of response times varies. If, for example, the average response time for a request is 4 seconds with one standard deviation of .2 seconds and two standard deviations of .5 seconds, then you know that the majority of your users are experiencing a response time between 3.5 and 4.5 seconds. On the other hand, if the average response time is 4 seconds, but one standard deviation is 2 seconds and two standard deviations equals 5 seconds, then you know that, although in general the response time is acceptable, a great number of users are experiencing poor performance. The average response time can be deceiving sometimes without knowing the distribution pattern of response times, and the standard deviation provides you with that insight. Once you have a strong understanding of these metrics for your requests for distinct time periods, such as every hour, every day, and every week, then you are ready to perform historical analysis against these metrics over time to try to identify trends. A common situation that I encounter during these exercises is a slight degradation of response time over several months as user load increases, but an increase in the standard deviation. So while the average response time may only increase from 4 seconds to 4.5 seconds, the number of users experiencing that reasonable response is becoming fewer and fewer. Usually before the response time for a particular request degrades substantially, the distribution of response times becomes increasingly volatile. The effective analysis of service request response times is the key to ensuring your SLAs. With the right methodologies and processes in place, you should be able to identify trends in response time and resolve their root causes before users are affected. Forecasting Forecasting begins by extrapolating trends to the point that they impact business functions and then applying additional business domain expertise to the trends to better understand their impact. Trending can be easily taught, and when performed frequently and methodically, it can become a very fruitful exercise, but proper forecasting can only come through experience and deep industry insight. For example, when building Java EE applications, the development team may be replacing J2EE 1.4 entity beans with Java EE 5 entity beans. The trends might extrapolate to excessive database interactions in three months, but because you know that the
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