3. PERFORMANCE ANALYSIS TOOLS › 3.3 Data Clustering Analysis

3.3 Data Clustering Analysis

Data Clustering using CA's U.S. patented Neugents(R)
technology, provided in CA MICS Performance Manager, enables
you to apply a clustering methodology to many types of
analysis.

In the past Workload Characterization was the analysis tool
provided with CA MICS Performance Manager.  Workload
Characterization is now the case study and Data Clustering,
using Neugents technology, is the tool. The Data Clustering
tool has been added to allow open ended analysis of any
CA MICS file and any elements contained in that file. With
this new feature you are not limited to a workload study, you
can cluster any data in any CA MICS file.

The discussion that follows will refer to Workload
Characterization using Data Clustering as the analysis tool.

Data Clustering using Neugents technology, provided in
CA MICS Performance Manager, enables you to apply a
clustering methodology to the analysis of your installation's
workload.  Data Clustering is an attractive approach to
performance management problems because it allows the number
of workload elements that need be considered in a study to be
reduced from tens of thousands to only a few.

Clustering methods are a statistical extension of scatter
plots to identify similarities and differences between
workloads.  Scatter plots are often difficult to prepare and
depend heavily on visual interpretation of the data.  The
need for visual interpretation limits the use of scatter
plots to two or perhaps three axes.  Clustering overcomes
these disadvantages through its ability to recognize patterns
in multiple dimensions.  Using the CA MICS database as an
input data source for clustering simplifies and extends the
application of the technique.

Note: Neugents technology is a prerequisite for using
Data Clustering.

The following sections describe the analytic techniques,
operational considerations, and reports used in the
application.

 1 - Functional Description
 2 - Usage Guidelines
 3 - Standard Output
 4 - Analytic Technique Tutorial
 5 - Component Operation
 6 - Case Studies
 7 - Requirements