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HP Capacity Advisor Version 4.1 User's Guide > Chapter 3 Key Capacity Advisor Concepts

Forecast Calculations

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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.

Whenever a Capacity Advisor report or profile is generated with an end date later than the current date, the historical utilization data must be projected into the future. The projection is indicated in the utilization graphs by a colored background. This projection is done based on a forecast model. Forecast models can be defined globally, for individual workloads or systems, for a scenario, and for individual workloads within a scenario. Because the process for defining a forecast model is basically the same regardless of where it is in the hierarchy of forecast models, the procedures below are broken into two parts: accessing the forecast model and defining it.

The Forecast Model Hierarchy

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

ForecastDescriptionOverrides
Global ForecastApplies to all workloads in Capacity Advisor for which a more specific forecast is not provided.
• Nothing
Workload ForecastApplies to a specific workload in Capacity Advisor unless a more specific forecast is provided.
• Global
Scenario ForecastApplies to all workloads within a Capacity Advisor scenario for which a more specific forecast is not provided.
• Global
• Workload
Scenario Workload ForecastApplies to a specific workload within a Capacity Advisor scenario.
• Global
• Workload
• Scenario

 

Forecast Model Attributes

Table 3-6 Forecast Model Attributes

FieldDescription
Forecast Data Range SelectionThe forecast data range defines the set of historical data on which the forecast will be based. The forecast data range can be specified as:
  • A fixed interval ending on a specific date

  • A fixed interval beginning on a specific date

  • The time interval between two dates

  • A fixed interval ending on the last full day of data collection

Automatically adjust the specified range to use existing dataWhen this feature is checked, forecasts are generated using the data that is available, even if the data is incomplete. When the existing data range is shorter than the specified forecast range, then the forecast range is shortened to fit the available data. Default: on (checked)

Unchecking the box prevents a forecast from being generated until a data range is specified that contains an interval of collected data sufficient to fill the forecast period.

Example: Assume that you want to view activity one week (7 days) into the future using the most recent week of data. However, in this particular week, only four days of data were collected. With this featured turned on, the future will be tiled with repetitions of the four days of data until the forecast interval is filled.

Annual Projected Growth RateSpecified in percent for each utilization resource measured (CPU, memory, network I/O, and disk I/O).

Use positive values for increasing utilization, negative values for decreasing utilization, or zero to indicate no change. Default: zero (0)

Annual project growth rates in force for a workload within a scenario are displayed in the Scenario Workloads Resource Utilization Table on the Edit Scenario Workload tab.

 

Combining the Data Range with the Annual Growth Rate:

The forecast is applied point-by-point to the historical data within the range specified by the user. 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 provided by the user 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.

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