CHAPTER 12 TRENDING, FORECASTING, AND CAPACITY PLANNING (Web server)
CHAPTER 12 TRENDING, FORECASTING, AND CAPACITY PLANNING as marketing activities and promotions, to determine whether model trends are the result of these factors, and hence temporary, or whether they present a long-term problem. Finally, when trying to understand the cause of a daily trend, always perform a sanity check by identifying changes in user load: more users will almost always increase resource utilization. Weekly models examine resource utilization behaviors during the course of your standard business week. Again, the demarcation of a business week is dependent on your business. As an example, an online retailer may observe a significant increase in user load, and hence increased resource utilization, on Saturdays and Sundays when people are home from work. Or the site might experience a spike in utilization on Friday afternoons when its customers are bored at work and pass time browsing the online store. Regardless of the cause of a weekly anomaly, the goal is to identify special behavioral periods throughout the week and trace those behaviors back historically to determine any trends in that behavior. Monthly trends, which may or may not be applicable to your environment, serve to examine resource utilization behavior during the course of a month. For example, consider the impact of paydays on an online retailer. Online retailers might have spikes in activity on the 15th and 30th of the month, on the 10th and 25th, and on every Friday (covering people paid every other Friday). If these spikes in activity exist, then you need to identify them and compare them to historical data to identify trends that may exist as results of increases in specific sets of application functionality. In the payday scenario, use of the application s catalog browsing functionality may increase a day before a payday, and online purchases may increase on the payday itself. Both of these activities execute separate paths of the application code, and thus may use resources differently: browsing may make more database queries, while purchasing may use more session space and interact with a third-party merchant for payment verification. Finally, annual models and trends examine the behavior of resources throughout the year. They attempt to predict when user load is the highest; for example, an online retailer in the United States may experience the most load between the day after Thanksgiving and Christmas Eve. Analyzing these behaviors is important, so that you are prepared to maintain SLAs during these peak usage periods. Annual trends are typically the easiest to analyze, because the time periods are so great between events, but for the same reason, their accuracy when moving into forecasting is inconclusive if you have less than five or ten years worth of data. Note When time differences are too great between measurements, and you have very few measurements, forecasting is usually not fruitful. Consider this scenario: have you ever tried to start a business? To do so, you construct a business plan, and as part of that plan, you build one-year, three-year, five-year, and sometimes seven-year projections defining the future of your company. Most companies do not make it to seven years (actually, most do not make it past the first year), but if they do, their financial state in seven years is almost certainly not in line with their initial business plan projections. However, if the time difference between measurements is short with respect to the life of the company, and you have a significant number of measurements, then the forecasts can be trusted. Consider a U.S. retail company that has been in business for ten years: it can trace its Christmas season growth patterns over a decade and very accurately predict what will happen in the following year.
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