1.3 Analysis Capabilities


The CA MICS Capacity Planner provides an extensive set of
analysis functions to perform workload characterization,
workload forecasting, and presentation graphics. The
facilities use the information contained in the CA MICS
database, and in the Capacity Planner database, as the input
to the analytic functions provided. These functions include:

o Profile and Trending (PFT): a facility that reads the data
  in the Capacity Planner database and generates compound
  growth rates for the data elements that you select. PFT
  interfaces with the Capacity Planner database definitions
  to provide an easy menu driven dialog that allows you to
  select the files and data elements that you would like to
  analyze. (See Chapter 5 for more information.)

o Simple Regression (SMR): performs linear regression on a
  user-selected data element in the Capacity Planner database
  and displays results in either a color or printer graphic
  format. This facility can be very useful for quick analysis
  and understanding the growth of a particular data element.
  (See Chapter 6 for more information.)

o Univariate Modeling (UVM):  an easy-to-use facility that
  allows you to perform complex statistical analysis against
  information in your Capacity Planner database that results
  in forecasts that can be saved back into the Capacity
  Planner database.  These forecasts can use any of the
  Capacity Planner file types (see Section 1.2).  When a
  forecast is saved, a forecast file is built that can be
  used as input to either Presentation Graphics or to MICF's
  presentation facilities.  (See Chapter 7 for more
  information.)

o Multivariate Regression (MVR): a statistical analysis
  facility that generates savable forecasts.  In contrast to
  Univariate Modeling, MVR can use the activity of data
  elements in different (or the same) Capacity Planner files
  to forecast the activity of another data element. These
  forecasts can be made available to Presentation Graphics or
  MICF. (See Chapter 9 for more information.)

o Business Element Forecasting (BEF): uses the
  information provided by a user-defined Business
  Element File as the basis for forecasting the activity
  of a computer resource metric contained in the Capacity
  Planner database. BEF allows you to discover relationships
  that may exist between business activity and computer
  resource consumption and use that as the basis of your
  workload forecasting studies. These forecasts can be saved
  for further analysis or as input to Presentation Graphics
  or MICF. (See Chapter 10 for more information.)

o Workload Characterization: a method by which computer
  resource consumers can be grouped into like categories.
  This facility uses data contained in the CA MICS database
  to find relationships that are made apparent through the
  application of statistical clustering techniques. These
  clusters (or groupings) can then serve as the basis for
  workload forecasting studies so you can plan the capacity
  needs of your enterprise. (See Chapter 11 for more
  information.)

o Presentation Graphics (PGR): a screen driven interface to
  a set of plotting routines that produce printer and color
  plots of information in the Capacity Planner database.
  These plots can include current and previous forecasts,
  confidence limits, and reference lines. These analyses can
  be tied into MICF Production Reporting so that they are
  generated as part of the update process for a capacity
  planning file. (See Chapter 4 for more information.)

o Worksheet Facility: a method by which users can
  communicate their forecasted system resource needs to
  you.  The Worksheets use files and data elements you
  select from the Capacity Planner database and format
  them into a report that you can distribute to your
  user base.  The users then fill in their anticipated
  resource needs and return these to you. A data entry
  facility enables you to input this information and
  update the Capacity Planner database with the
  forecasts.  The Worksheet Effect Report processes the
  information from the worksheets and combines it with
  actuals data and generated forecasts into a
  consolidated report that reflects anticipated and
  actual resource activity. (See Chapter 8 for more
  information.)

Note: SAS/STAT is a prerequisite for using SMR, UVM, MVR,
BEF, and Workload Characterization.  SAS/GRAPH is a
prerequisite for using the graphics capabilities of PGR.

o Resource Component Analysis using CA's U.S. patented
  Neugents Technology (NCA):  a statistical analysis tool
  that permits the quantification of effects of a series of
  resource components against a specified analysis element.
  For example, processor busy is a function of the resources
  used by a series of workloads.  The Resource Component
  Analysis application can determine which of the workloads
  has the greatest impact on processor busy, and provide a
  weighted measure of the impact.  This facility can be used
  to prepare for workload forecasting operations or as a
  general data analysis tool.  (See Chapter 13 for more
  information.)

o Workload Forecasting Analysis using Neugents Technology
  (NWF):  a statistical analysis facility that generates
  savable forecasts.  This facility works in a similar
  fashion to the Multivariate Forecasting facility mentioned
  above but uses a set of CA patented techniques to permit
  you to develop nonlinear models that relate different
  resource consumption data elements from one or more
  capacity planning database files. This forecasting
  capability provides for the investigation of possible
  relationships among different resource variables, and the
  forecasting of future requirements of a resource variable
  based on estimates of the future requirements of one or
  more different resource variables.  (See Chapter 14 for
  more information.)

o Workload Characterization using Neugents Technology (NWC):
  a method by which computer resource consumers can be
  grouped into like categories.  This facility uses data
  contained in the CA MICS database to find relationships
  that are made apparent through the application of
  statistical clustering techniques. These clusters (or
  groupings) can then serve as the basis for workload
  forecasting studies or can be used as input for additional
  analyses.  This facility is similar in functionality to the
  Workload Characterization mentioned above, but utilizes a
  set of CA patented techniques to perform the clustering
  operation.  (See Chapter 15 for more information.)

Note: Neugents Technologies, provided at no additional cost,
is a prerequisite for using NCA, NWF, or NWC.


All of these analytic facilities are implemented through a
set of similar dialog interfaces, reducing the time to
productive usage. They also have extensive tutorial support
to assist you in their usage and the case studies found in
this guide provide practical examples of using these
facilities with actual data.