|
|
United States-English | |
|
|
 |
|
HP Capacity Advisor Version 4.0 User's Guide > Chapter 3 Key Capacity Advisor Concepts
Trends and Forecasts |
|
|
Understanding the trends in collected utilization data can provide insight into possible future requirements. These potential future requirements can be used to generate forecasts for planning. HP Capacity Advisor provides tools for analyzing utilization data to calculate trends from the data and to combine existing utilization data with projected trends to produce forecasts.
Determining trends from collected utilization data can be a challenging task. Accurate trend analysis requires adequate historical data and an understanding of the cyclic nature of the data being analyzed as well as any special events that might be found in the historical data.
Any algorithmic analysis must be able to deal with these problems. HP Capacity Advisor combines aggregation of points based on known business cycles to deal with cyclic patterns with exclusion of points to deal with special events, to provide data for a linear regression. To reduce the impact of cyclic changes in the historical data, a user-specified business period is used to break the data into time-interval based “bins” and each bin is then represented by a single point. The point can be the average, the peak, or the 90th percentile of the data (90% of the points are less than the value). A bin will not be used unless the percent of points within the bin that are valid exceeds the threshold you have specified.
It is crucial to have a significant amount of data for analysis. Choosing an appropriate business interval with a data collection period that is long enough helps to ensure that you have enough data for a useful analysis. For example, a business period of 1 week and data collection period of 1 month provides only four aggregate data points. This is insufficient to provide meaningful results. To improve results, for this example, use a business interval of 1 day with a data collection of 1 month to provide 30 data points, or use a business interval of 1 week with a data collection of 6 months to provide 26 data points. Modifying the business interval and/or the data collection period gives you more flexibility in arriving at a significant amount of data for analysis. You can set the report period to exclude a special event or mark the time period invalid to exclude points collected during that period from a trend analysis. Within any data collection period, events can occur in the polled systems that affect the quality of data available during that time period. Capacity Advisor identifies data points that could adversely affect the quality and validity of report results. The following are examples of events that Capacity Advisor can recognize (and disregard) as potential sources of invalid points:
How this relates to setting a Validity Threshold The Validity Threshold that you set should reflect your tolerance for obtaining a sufficient amount of valid data in the collection period that you designate. If the reports that you run show that the given validity threshold is not obtainable for the designated time period, this may indicate that many of the data points in the designated collection period are invalid. In this case, you can choose a lower Validity Threshold with the understanding that the report outcome may be a less reliable indicator of probable resource usage, or you can select a different or longer data collection period to improve the likelihood of obtaining a sufficient percentage of valid points for a good report. The linear regression is based on a least squares fit that minimizes the sum of the squares of the vertical offsets between each of the aggregate points and the trend line that describes them. You can choose to include error analysis in the report. The following error value is available: r-squared: r2 is the square of the correlation coefficient (r), and is used in the 'goodness of fit' analysis of trend estimations. r is a value between 0 and +/- 1. where values approaching +/- 1 indicate increasing validity of the data representation. HP Capacity Advisor forecasting allows you to combine a range of historical data (the forecast data range) with a predicted trend (the annual projected growth rate) to produce a forecast model. The forecast model can be used to provide an estimate of future utilization. The forecast model can be specified at four different levels within Capacity Advisor, with more specific forecast models overriding more general models, as indicated in the following table: Table 3-5 Forecast Models
The forecast data range defines the historical data that is combined with the annual projected growth rate to produce the forecast model. The forecast data range can be specified as: The annual projected growth rate is specified in percent and can be positive for increasing utilization, negative for decreasing utilization or zero for no change. No change is the default. Separate rates can be specified for memory and CPU growth. The forecast is applied point-by-point to the historical data within the range you have specified. It is applied linearly, so that a point 1 year from the starting point of a forecast is the result of the full growth rate being applied to the data. The data within the range you have provided is used to “tile” the future by applying the portion of the growth rate appropriate to each point to each point in the data range and repeating the data set until the desired end point is reached. |
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
 Printable version |
|
|
|
|
|
|||||||||||||||
|
|||||||||||||||
